Real time failure analysis and accurate warranty claim assesment

ABSTRACT

A method for failure analysis and warranty claim assessment, the method may include sensing sensed vehicle parameters by multiple vehicle sensors that include multiple types of sensors; calculating, by a vehicle monitor, based on the sensed vehicle parameters, parameters of multiple vehicle components; wherein the vehicle monitor is mechanically coupled to a vehicle or installed in the vehicle; determining, by the vehicle monitor and based on the parameters of the multiple vehicle components, whether the operation of the vehicle exceeds a warrantable operation of the vehicle; and evaluating, by the vehicle monitor and based on the parameters of the multiple vehicle components and by the vehicle monitor, whether a vehicle failure resulted from an operation of the vehicle that exceeds the warrantable operation of the vehicle or will result from the operation of the vehicle that exceeds the warrantable operation of the vehicle.

RELATED APPLICATIONS

This patent application claims the priority of U.S. provisional patentSer. No. 62/277,976 filing date Jan. 13 2016, U.S. provisional patentSer. No. 62/277,977 filing date Jan. 13 2016, U.S. provisional patentSer. No. 62/277,981 filing date Jan. 13 2016, U.S. provisional patentSer. No. 62/277,982 filing date Jan. 13 2016, and U.S. provisionalpatent Ser. No. 62/277,988 filing date Jan. 13 2016, all areincorporated herein in by their entirety.

BACKGROUND

There is a growing need to provide improvements in the fields of vehiclemonitoring, application approvals, service improvement, productimprovements, failure analysis and warranty claim assessment, and newfeature launch.

SUMMARY

Systems, vehicle monitors, methods and computer program products asillustrated in the specification and/or claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 illustrates a system and its environment according to anembodiment of the invention;

FIG. 2 illustrates a method according to an embodiment of the invention;

FIG. 3 illustrates a vehicle according to an embodiment of theinvention;

FIG. 4 illustrates a graph;

FIG. 5 illustrates a vehicle monitor according to an embodiment of theinvention;

FIG. 6 illustrates a method according to an embodiment of the invention;

FIG. 7 illustrates a method according to an embodiment of the invention;

FIG. 8 illustrates a method according to an embodiment of the invention;

FIG. 9 illustrates a vehicle monitor according to an embodiment of theinvention;

FIG. 10 illustrates a method according to an embodiment of theinvention;

FIG. 11 illustrates a method according to an embodiment of theinvention;

FIG. 12 illustrates a method according to an embodiment of theinvention;

FIG. 13 illustrates a method according to an embodiment of theinvention;

FIG. 14 illustrates a method according to an embodiment of theinvention;

FIG. 15 illustrates a method according to an embodiment of theinvention;

FIG. 16 illustrates a method according to an embodiment of theinvention;

FIG. 17 illustrates a method according to an embodiment of theinvention;

FIG. 18 illustrates a method according to an embodiment of theinvention;

FIG. 19 illustrates a method according to an embodiment of theinvention;

FIG. 20 illustrates a method according to an embodiment of theinvention;

FIG. 21 illustrates a method according to an embodiment of theinvention;

FIG. 22 illustrates a method according to an embodiment of theinvention;

FIG. 23 illustrates a system according to an embodiment of theinvention;

FIG. 24 illustrates a method according to an embodiment of theinvention; and

FIG. 25 illustrates a method according to an embodiment of theinvention.

DETAILED DESCRIPTION OF THE DRAWINGS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

Because the illustrated embodiments of the present invention may for themost part, be implemented using electronic components and circuits knownto those skilled in the art, details will not be explained in anygreater extent than that considered necessary as illustrated above, forthe understanding and appreciation of the underlying concepts of thepresent invention and in order not to obfuscate or distract from theteachings of the present invention.

The terms “computer program product” and “computer readable medium” areused in an interchangeable manner and can replace each other.

The phrase “and/or” means additionally or alternatively.

Any reference to “comprising” or “comprises” or “includes” or“including” should be interpreted as also applying to “consisting”and/or to “consisting essentially of”. For example—a method that isillustrated as including certain steps may be limited only to thesesteps, may have additional steps or may have additional steps that donot materially affect the basic and novel characteristic(s) of themethod.

The terms “truck” and vehicle are used in an interchangeable manner andcan replace each other. Accordingly—any reference to a truck or avehicle should be applied to trucks as well as other vehicles such asbuses, cars, marine vehicles, and the like.

The terms “vehicle monitor” and “telematics system” are used in aninterchangeable manner and can replace each other. A non-limitingexample of a telematics system is the telematics system of TraffilogIsrael. The vehicle monitor may be a part of a telematics system or maybe the telematics system itself.

The terms “vehicle system parameter”, “vehicle component parameter” areused in an interchangeable manner—and one term can replace the other.

Any combination of any steps of any of the method illustrated in thedrawings and/or the specification can be provided.

A vehicle monitor capable of executing any combination of any steps ofany of the method illustrated in the drawings and/or the specificationcan be provided.

Any but width, size, frequency, number are provided only as an example.Any example in the specification is a non-limiting example.

Accurate Application Approval

The term application means recommendations, suggestions and/orinstructions about how a customer will use a particularly configuredvehicle.

Vehicle original equipment manufacturers (OEMs) typically should approveapplications before a vehicle can be sold.

There applications provided by vehicle OEMs are based on duty cycles.

There is a growing need to provide accurate duty cycle measurements.

There are provided systems method and computer program products forevaluating vehicle configurations when the vehicle is operated accordingto a given application (also referred to as “application of interest”).The evaluating is done in a reliable manner, based on duty relatedparameters of multiple vehicle components.

The method can be executed (fully or in part) by a vehicle monitor thatis mechanically coupled to the vehicle and/or is installed in thevehicle.

The vehicle monitor may process a vast amount of information—especiallya vast amount of sensed vehicle parameters. These sensed vehicleparameters may be sensed by multiple vehicle sensors. The vehiclesensors may be included in the vehicle and/or mechanically coupled tothe vehicle. The vehicle sensors may include multiple types of sensors.

For example a truck can be monitored by tens and even more than hundredsensors. For example—engine/after treatment sensors—between 20 to 30sensors; transmission sensor (automated and/or manual)—between 7 to 10sensors, driveline sensors (tandem axle) between—4 to 6 sensors; brakessensors (tandem axle, disc brake)—between 21 to 24 sensors; Tires/Wheelssensors—12 sensors; high voltage supply (HVAC) sensors—between 4 to 6sensors; telematics/Infotainment/Navigation—6 sensors, Body/Lightingsensors—between 8 to 10 sensors; ADAS—between 6 to 10 sensors andtrailer sensors—between 6 to 8 sensors—totals 88 to 122 sensors. Thedifferent number of sensors may result from regional differences becauseof emissions and safety regulation.

This vast amount of information could not be transmitted from thevehicle to a remote computer located outside of the vehicle because ofbandwidth constraints and/or because of lack of reception in variousregions in which the vehicle travels. Alternatively—the cost associatedwith the transmitting the vast amount of information would render thetransmission too costly.

The vehicle monitor may be configured to determine, based on the sensedvehicle parameters, duty related parameters of multiple vehiclecomponents.

The vehicle monitor may also be configured to calculate the performanceof the vehicle when configured according one or more vehicleconfigurations—when the vehicle is operated according to a givenapplication.

The calculation of the performance of the vehicle may be based on, atleast, (i) the duty related parameters of the multiple vehiclecomponents and (ii) relationships between the duty related parameters ofthe multiple vehicle components and the performance of the vehicle.

Alternatively, the vehicle monitor or another vehicle transmitter maytransmit the duty related parameters of the multiple vehicle componentstowards a receiver located outside the vehicle and the duty relatedparameters of the multiple vehicle components may be processed by asystem or computer such as a remote computer (a computer that is notincluded in the vehicle).

The remote computer may calculate the performance of the vehicle, andthe calculation can be based on, at least, (i) the duty relatedparameters of the multiple vehicle components and (ii) relationshipsbetween the duty related parameters of the multiple vehicle componentsand the performance of the vehicle.

The aggregate size of the duty related parameters of the multiplevehicle components is a fraction (for example less than 1/100 or 1/1000or 1/10000 of 1/1,000,000) of the aggregate size of the sensed vehicleparameters. The duty related parameters may reflect S-N curves ofcomponents that are subjected to cyclic fatigue failure or may be anycompressed representation of the sensed vehicle parameters. Forexample—a single duty related parameter may represent the values ofsensed vehicle parameters over a period of time.

Due to the size difference the transmission of the duty relatedparameters of the multiple vehicle components is cost effective and doesnot impose unreasonable limits of the bandwidth.

It should be noted that the vehicle monitor may evaluate theperformances of multiple vehicles configurations (when the vehicle isoperated according to the given application) and may select a particularvehicle configuration. The selection may be based on the performances ofthe vehicle configurations and on the given application.

The selection can be made by the remote computer.

The monitoring and/or calculation of the duty related parameters of themultiple vehicle components provides a real estimate of the actualapplication (the actual manner in which the vehicle is used by a client)and may be used for selecting the vehicle configuration that may fitthat given application.

Typically, vehicle buyers tend to underestimate the duty of theirvehicles undergo (are too optimistic regarding the application) andvehicle OEM tend to overestimate the duty that their vehicles undergo.The monitoring and/or calculation of the duty related parametersprovides a real estimate of the load that is undergone by the vehicle,and this real estimate may assist both parties in finding the vehicleconfiguration that will best match the actual application.

Accordingly—vehicle OEMs can improve the product/part selection,accuracy and speed of application approvals by using measured duty cycledata from the given application.

This benefits vehicle OEMs by having greater certainty that the approvedvehicle will perform well in the given application. This benefits thevehicle buyer by assuring that the vehicle is neither over-specified orunder-specified and is, therefore, the most cost-effective solution.

OEMs can also improve the speed of application approval by implementingthis concept with an online facility for analyzing applications (e.g.web site with help desk, to be used by the vehicle salesman).

This analysis can also offer vehicle users an accurate estimate of fueleconomy, vehicle durability and other performance parameters ofinterest. This further analysis assists vehicle users in selecting theoptimal vehicle for their application and in modeling out how theirbusiness will perform when using a particular vehicle.

This capability also opens up new business models by including financingoptions and warranty coverage options as part of the applicationapproval.

There is a provided a system, method and computer readable medium thatis configured to determine (a) if an application is approvable, (b) ifan application meets customer needs for payload, fuel efficiency andother factors, and/or (c) if an application is otherwise optimal (e.g.in the sense of maximizing weighted utility functions for all parametersof interest),

The relationships between the duty of the vehicle (or duty of componentsof the vehicle and/or duty of different systems of the vehicle) and theperformance of the vehicle can be represented in various manners, suchas but not limited to a performance model of the vehicle.

The performance model of the vehicle may be generated based on analyzingdata from at least some of the following sources:

a. OEM system and sub-system durability data or usable life estimatesgenerated by engineering analysis and laboratory testing.b. Monitoring system supplied, field empirical, system and sub-systemdurability data or usable life estimates generated by comparing wear-outand repair history to vehicle duty.c. A list of plausible vehicle configurations or vehicle configurationsacceptable to the customer.d. After sales inputs by using the system and by after sales externaldata like: Real Parts mean time between failure MTBF. Customer realmissions. Real shop downtime. Aftermarket users (parts MFRSs) todemonstrate parts durability. Aftermarket sources of component data forcommon parts.

The performance model of the vehicle may be generated by at least one ofthe following:

a. Analysis methods for combining analytical, laboratory or empiricalfield data and OEM and user targets for application performance toproduce application performance models.b. Statistical correlation methods (e.g. linear regression, regressionagainst a non-linear function),c. Physics-based simulation methods where a physical model is calibratedusing analytical, laboratory or field empirical data,d. Using optimal observers to correlate or calibrate steps a or b above.

The system performance model may run with the measured duty to produceratings of the different configurations against OEM and customer needs(e.g. projected warranty cost, projected down time, projected fuelefficiency, etc.).

Multiple configuration may be evaluated.

The multiple configurations may be provided in at least one of thefollowing manners:

a. Manual modification of configurations, where the user is in the loopchanging configurations to seek better results.b. Automated modification of configurations using Monte Carlo, partialderivative optimization or other techniques to seek better results.c. Massive parallel analysis of all possible configurations andsubsequent sorting/searching for optimal configurations.

As indicated above, the method may be executed by the vehicle monitoralone or by both the vehicle monitor and a remote computer.

The vehicle monitor is installed in a vehicle (or coupled to thevehicle) and is configured to sense, compute, provide a user-interface.The vehicle monitor may include a computer, one or more sensors and acommunication unit. The computer runs software that observes sensorscomputes parameters of interest based on sensor observations (sensorsmay be part of the vehicle or added on after the vehicle ismanufactured).

Parameters of interest are then used directly or combined mathematicallyand then used in the following ways:

a. To trigger an event such as: an alarm, a modification of datacollection methods, a transmission of collected data, or a number ofother actions,b. Averaged or filtered to produce a lower bandwidth signal or reduceout of band noise.c. Stored in local memory.

The vehicle monitor may execute software that may include a real-timeoperating system with hardware support and a dynamically re-programmableapplication layer. The back-office computing system manages versioningand download of both real-time operating system and the dynamicapplication layer.

The vehicle monitor may include a communication unit and/or be coupledto a communication unit such as such as a cellular data communicationunit and/or an Internet data communication unit—that allows datacommunication between in-vehicle platform and other entities such as aback-office facility. The communication unit of the vehicle monitor canconnect directly with a mobile network, satellite network or anywireless network.

Additionally or alternatively, the communication unit may communicate(by short range communication) with a unit of the vehicle (such as amultimedia center, radio, or any unit that is installed in thevehicle—such as the smartphone of communication unit of a driver of thevehicle). The unit of the vehicle may communicate with the mobilenetwork, satellite network or any wireless network.

The remote computer is not a part of the vehicle and may include, forexample, a back-office computing, data storage system, and proactivecall center—runs offline and near-real time applications to provisionin-vehicle units, analyze data from in-vehicle units, store data fromin-vehicle units and analysis, create reports, host web applications,etc. The call center both notifies customers of problems and receivescalls from customers with questions. The back-office computing may byany type of computer and may include one or more servers, one or moredesktop computers, one or more laptop computers, a mobile device, andthe like. The other computer may be located within a cloud computerizedenvironment.

The vehicle monitor may have a campaign structure, and can providebetter information by measuring both long-term steady-load conditionsand by capturing short-term high-load (overload, damage-cycle)information. Any of the vehicle monitors illustrated in thespecification may or may not have a campaign configuration.

A campaign configuration is the ability of the vehicle monitor tocollect, categorize and report machine data related to a particularaspect of the machine's operation (vehicle parameters, vehicle componentparameters, vehicle system parameters).

These aspects may include, for example, maintenance, wear-state of themachine, hydraulic usage, overall machine duty and many others. Datafeeds into the campaign structure from multiple sources (sensors,computed values, data from other ECUs) and at high data rates.

For some sensors, such as GPS, this could be as low as 10 samples/sec.Most sensors, related to engine, transmission and suspension(accelerometers, vibration, shaft angular displacement) will need to besampled at 10000 samples/sec or higher rates. For 16-bit data, which istypical, this amounts to 144 GB per year of data for each sensormonitored (2000 hours of operation per year).

A typical installation will require monitoring ten different sensors,which generates 1.4 TB of data per year per truck. For a 1000 truckfleet this is 1.4 PB of data. Data is then categorized and accumulatedto produce an overall view of some aspect of the machine's operation.For example, over the course of operation, a truck's suspension mayundergo millions of stress cycles.

Each stress cycle is measured by high-bandwidth sensors (e.g.accelerometers) sampled 10000 times a second. This data is reduced to asingle parameter indicating the peak magnitude of the stress cycle. Thisparameter is then categorized by size and then an accumulator of similarsized stress cycles is incremented. This data reduction step reduces thedata size by seven orders of magnitude or more. The resulting stresscycle accumulators are transmitted to the infrastructure and are used tocategorize the suspension duty and wear-state.

In this example, the stress cycle accumulator may have 100 buckets(stress ranges), each of which would be a 32-bit integer.

If the accumulator is transmitted once per day this is 400B per day or20,800B per year, which is a large reduction over the 144 GB/year forunprocessed data (7,000,000:1). This data includes of one hundred 32-bitcounters each of which indicates how many times a given peak stresslevel was achieved for a particular small range of stress values. Thesmallest stress level range “bucket” to the largest stress level rangespans the range of stress levels seen in the field.

In other examples, data reductions by means other than peak detectionare required. We may use high-bandwidth sensor data to perform anongoing calibration of a computer model intended to predict a systembehavior of interest. In a simple, case this could be a best fit line tomatch the incoming data from a sensor, sampled 10000 times a second,calculated every minute. This reduces the data rate from 1.2 MB perminute to 6B per minute (16 bit values of line slope, line intercept anda 16-bit goodness of fit indicator such as R-squared).

For example, the data collection and categorization process can take anumber of forms such as:

a. Simple digital input events—accumulate the total time of digitalinput, average duration of digital inputs, number of digital inputsevents, or vehicle distance traveled during digital input. Alsoaccumulate digital inputs logically combined with count, time ordistance traveled parameters. For example: count the number of times thebrake is pressed, the number of times the brake is pressed more than 10seconds, or the number of times the brake is pressed in a day.b. Combined digital events—Form and accumulate a logical combination ofthe above simple digital events with the logic operators AND, OR andNOT. Also accumulate combined digital events logically combined withcount, time or distance parameters.c. Analog Parameter Statistics—Accumulate common statistics (mean,median, standard deviation, distribution and density). Combinestatistics and pre-defined constants using relational operators (<, >,=, ≦, ≧, ≠) to create digital events that can be used as describedabove.d. Analog Parameters Statistics Between Periods—Compare analog parameterstatistics between different pre-defined periods of time using methodsas described above. For example, count the number of times that theaverage daily engine oil pressure is more than 1 bar lower than theprevious day.e. Analog Parameters Statistics Between Vehicle Type Groups—Compareanalog parameter statistics between pre-defined vehicle groups usingmethods described above.

Traditional vehicle monitors measure only a few vehicle parameters, suchas DTC status, vehicle location or vehicle speed. Measurements are madeat a low repetition rate. These few parameters are sent via a cellulardata channel to the cellular service provider's storage facility.Analysis of the data is made in a non real-time manner for thepreparation of reports.

By contrast the suggested vehicle monitor measures nearly all vehicleparameters at whatever repetition rate is required to learn the systembehavior.

Further the suggested vehicle monitor can combine parameters to deducesystem states that are not measured directly.

Depending on the needs of a particular measurement situation, thesuggested vehicle monitor can process data in real-time on the truck orin near-real time in the back office. This allows the suggested vehiclemonitor to measure parameters at a high rate, make analysis locally inthe truck and then send relatively small amounts of data over cellularchannels. This reduces the cost of cellular data transmission whileinsuring that all of the relevant data is captured.

The suggested vehicle monitor also stores background data on componentperformance and life to assist the OEM in calibrating a system/componentlife model used to estimate component life for each truck configurationbeing investigated. See the drawing below for one possible embodiment ofthe invention.

The suggested vehicle monitor maintains a detailed history of how aparticular vehicle has been used. By comparing this detailed use historyto known component failures and known repair history the vehicle monitorprovides OEMs with a knowledge base of component life. This knowledgebase can then be used to calibrate component life models used in productdesign and application approval.

FIG. 1 illustrates multiple vehicle monitors 20(1)-20(N) of vehicles80(1)-80(N) respectively. The multiple vehicle monitors 20(1)-20(N)provide information, via network 90, to a computerized system(illustrated as including multiple computers 30(1)-30(M)) thatparticipate in the evaluation of the application. In this configuration,information about each individual vehicle and about the compositeperformance of all vehicles is available for analysis. Thisconfiguration also allows optimal use of computing resources and optimaluse of data communication service. Alternatively, the analysis is done,partially or fully by the vehicle monitors themselves. The vehiclemonitors may form a distributed network of computers that may performthe analysis in a distributed or centralized manner. For example—onevehicle monitor may analyze information supplied by another vehiclemonitor. Load balancing techniques may be applied between the vehiclemonitors.

FIG. 2 illustrates a method 100 according to an embodiment of theinvention.

Method 100 may include:

a. Step 102 of gathering supplier engineering data such as test-benchderived S-N curves or ultimate strength limits for components ofinterest.b. Step 104 of gathering actual usage information for an application ofinterest (for example duty related parameters such as annual ton-mileshauled, average engine load, or peak engine load).c. Step 106 of processing actual usage information (gathered during step104) from vast number of measurements of vehicle monitors using big datatechnique to provide big data analysis results. In particular, we arelooking for correlations between any of the measured parameters and theperformance of a component of interest to create an empirical predictiveengineering model of the component's performance. We also analyze thebig data to calculate the truck state parameters that are not directlymeasured. For example, the total energy dissipated in the brakes is notdirectly measured, but is calculated from truck speed, truck altitudeand brake actuation data pulled from the big data set.d. Processing (step 108) the big data analysis results, the supplierengineering data and initial proposed configurations (gathered in step105) by an engineering model of vehicle systems. The engineering modelof a system relates usage of the system to the system's performance, inparticular, to the system's degradation with time. For example, in amechanically stressed component, this model relates the number of stresscycles to the probability of component failure.e. Processing (step 107) the big data analysis results by an empiricalmodel of the system. For example, this may be done by making a study ofcorrelations between usage and component performance data to findrelationships between vehicle usage and component performance.f. Step 109 of gathering data describing a fleet's economic model andusing this data to rank and proportion different competing economicfactors (e.g. by understanding the tradeoff between fuel cost vs. driverpay vs. cost of capital, we can understand what is the optimal truck interms of fuel efficiency, fuel efficiency degradation at higher speedsand vehicle cost, all of which can be calculated from the engineeringand empirical models).g. Rating (step 110) configurations based on the outcomes of steps 107,108 and 111. System performance from steps 107 and 108 are scaled byutility functions from step 111 and added up to provide rankings of thedifferent configurations. The ratings are a comparison of projectedsystem performance against OEM and customer goals. For example, an OEMmay have a 500,000 mile durability goal for a given system. If thesystem model, when run against the customer's duty cycle for 500,000miles, predicts a system failure then the application receives a lowrating.h. Step 111 includes applying a utility function of competing functionson fleet economic model (gathered in step 109) and on cost andperformance trade off data from OEM (step 112). This utility functiondetermines the optimum balance between directly competing goals betweenvehicle OEMs and fleets (e.g. vehicle price, OEMs want a higher price,fleets want a lower price). The utility function also setsdiminishing-returns limits on non-competing goals (e.g. improvedreliability is generally beneficial to OEMs and fleets, but only up tothe point that it improves OEM pricing or reduces fleet operatingcosts).i. The outcome of step 110 is fed to decision step 113 of checking ifgoals are met and if so—the application is approved (step 115).j. Else—step 113 is followed by step 114 of generating alternativeapplications, goal directed search and jumping to step 108.k. Goals used in step 113 are supplied by OEMs and customers. Forexample, an OEM may have a goal of 500,000 miles component useful life.If the analysis indicates that a component does not have 500,000 milesuseful life when used as the customer intends, the goal is not met.Customers may have goals related to cost of operation or frequency ofmaintenance and repairs. The goal generation may feed the rating ofconfigurations.l. Step 114 generates new system configurations to evaluate when goalsare not met with previous system configurations. For example, we cangenerate new truck configurations using the OEM truck configurationsoftware that validates major component selections such as engines,transmissions, drivelines, brakes and axles.m. New configurations can be generated in a number of ways including:user directed changes to configurations, randomly generated changes toconfigurations, or analytically, goal directed, changes toconfigurations (e.g. finding maxima of ratings by analysis of partialderivatives of ratings with respect to inputs).

The steps of method 100 may be de-serializing using mass analysistechniques.

Method 100 may be executed by a computer, a vehicle monitor, and thelike.

FIG. 3 illustrates a vehicle 300.

The vehicle 300 includes vehicle monitor 310, communication unit 360,controllers 330, and sensors 340, 342 and 344 as well as vehicle systemsand/or vehicle components 350.

Sensors 344 are coupled to the vehicle monitor 310 but not tocontrollers 330. Sensors 330 are coupled to controllers 330 but not tovehicle monitor 310. Sensors 342 are coupled to vehicle monitor 310 andto controllers 330. Any combination of couplings between sensors andother units/modules may be provided.

The controllers 330 are configured to control various vehicle systems. Asingle controller may control a single vehicle system—but this is nonecessarily so and one controller can control multiple vehicle systemsand/or multiple controllers can cooperate to control a single vehiclesystem.

Non-limiting examples of vehicle systems include engine, transmission,brakes/antilock brakes, electronic stability control, axle/driveline,suspension/ride height control, tires/tire inflation monitor/control,instrument panel/driver interface.

Sensors 340, 342 and 344 may include at least some out of the followingcontrollers: ambient air temperature sensor, absolute pressure sensor,tire pressure sensor, tire temperature sensor, wheel speed sensor,propeller shaft speed sensor, engine speed sensor, engine coolanttemperature sensor, engine coolant pressure sensor, engine oil pressuresensor, engine oil temperature sensor, engine inlet air temperaturesensor, engine inlet air pressure sensor, engine inlet air temperaturesensor, engine exhaust temperature sensor, engine exhaust pressuresensor, 3-axis chassis acceleration, steering sensor, braking sensor,throttle position sensor, ride height sensor, suspension inflationpressure sensor, clutch sensor, height sensor, location sensor (GPS),and the like.

Vehicle monitor 310 may receive sensor data from sensors 322 and 334.Vehicle monitor 310 may receive from controllers 330 sensor data (fromsensors 330 and/or 332) and/or may receive computed values fromcontroller 330. The processed data and/or the sensor data may includeduty related parameters.

An example of sensor data that may be provided from controllers 330 mayinclude, for example, engine coolant temperature or engine revolutionsper minute (RPM). Examples of the computer data may include enginetorque, engine power, and engine fuel rate that are calculated by thecontrollers 330 based on mathematical combinations of sensor data.

The communication unit may be configured to perform short rangecommunication (especially within the vehicle) and/or long rangecommunication (especially between the vehicle and networks locatedoutside the vehicle). The long and short communication may be managed bydifferent communication units of the vehicle. For example—the vehiclemonitor may include a short range communication unit that wirelessly (orin a wired manner) communicates with another communication unit of thevehicle that may also manage long range communication.

Vehicle monitor 310 may include a processor, a memory unit and multiplecommunication ports. The processor may be a general purpose processor, adigital signal processor, a FPGA, a microcontroller, and the like. Forexample, the processor may include a low power micro controller unit ofST microelectronics.

The vehicle monitor may measure the actual duty of the application ofinterest. The actual duty may be reflected by duty related parameters ofmultiple vehicle components. The vehicle components may be include inany of the vehicle systems and may include, tires, chassis components,engine components, and the like. A vehicle component may be mechanicalcomponents, and electrical component or any other component of thevehicle.

The actual duty of the vehicle may be measured directly from sensorswith simple (meaning directly measured from aphysical-parameter-to-voltage transducer) parameters such as: vehiclemileage, amount of time at a power level (histogram of time vs. power),amount of mileage at a specific load (histogram of mileage vs. load),amount of time on at a given level of road roughness (RMS measure ofvertical acceleration) and other directly measured quantities.

These measurements are made using sensors commonly fitted (many sensorsare commonly fitted on vehicles today for reasons other than telematicsmeasurements, e.g. inlet air pressure, inlet air temperature are used tocontrol fueling rate on engines) on vehicles such as odometers,accelerometers and using data from the engine's electronic control unit(e.g. engine speed, engine load). These parameters are measured,recorded, and stored by the vehicle monitor. These sensors commonlysupply data via CAN bus messages.

It should be noted that the terms “CAN”, “CANBUS” and “CAN bus” are usedin an interchangeable manner.

In addition to this, the actual duty of the vehicle is also estimated bycombining measured parameters and mathematical engineering models of thevehicle's hardware.

In the example that follows, the vehicle monitor combines vehicle speed,vehicle altitude, engine torque with brake actuation/retarder actuationcommand information to form a measure of energy dissipated in the brakesor retarder.

For example, accumulated brake system energy dissipation is a measure ofbrake duty. Brake system energy dissipation is determined by observingthe change in the vehicle's total (potential and kinetic) energy beforeand after a braking event, then subtracting to form the difference inenergy.

The energy difference is due to braking effort. The braking effortenergy of each braking event that the vehicle undergoes is added to anaccumulator that holds the measure of total energy dissipated by thebrakes for all braking events.

The vehicle's change in kinetic energy is calculated using an estimateof the vehicle's mass and the vehicle's measured speed before and afterthe braking event (KE=½mv̂2).

The vehicle's change in potential energy is calculated from thevehicle's mass and the change in altitude the vehicle underwent duringthe braking event (PE=mgh).

The vehicle's mass is typically estimated by the transmission controllerand the vehicle's change in altitude is measured by a global positioningsystem {GPS} altimeter (also referred to as “GPS unit”). In applicationsthat have the possibility of engine braking or retarders, the energyabsorbed by these devices also needs to be considered, both on their ownas components and as offsets to the energy dissipated in the brakes. Thedeployment of brakes, engine brake or retarder is known from CAN busmessages describing the braking event.

For example, in some ways similar to the brake energy example above, thevehicle monitor can estimate overall engine duty by measuring the amountof work that the engine has done in total and in between maintenanceevents. This is conveniently measured as kilowatt-hours and has theunits of energy.

Measure the engine power, in kilowatts, every second (kilowatt seconds),multiply by 3600 to get kilowatt-hours. Add this value to an accumulatorkeeping the engine's total kilowatt-hour total.

The vehicle monitor can also measure how much energy is produced at agiven power level (work rate) to account for accelerated engine wear athigh power levels. In this case, the vehicle monitor can keep an ongoingtally of time at a given power level (bucketed into small ranges ofpower). This data can then form a histogram that shows the amount oftime at a given power level. The area under each bar in the histogram isenergy produced at that power level. Engine wear increases dramaticallywhen operated at high power levels. The time-at-power histogram gives agood picture of the engine's wear state. In this case, the engine'selectronic control unit supplies engine speed, torque and powermeasurements.

For example, for manual transmission, to gain an accurate understandingof transmission duty, the vehicle monitor may need to measure the amountof energy that the engine has put into the transmission in total andseparately for each gear range available in the transmission. To do thisthe vehicle monitor continuously observes the engine's output power(supplied by a controller such as engine control unit {ECU} over the CANbus), the gear range of the transmission (supplied by a controller suchas the transmission control unit {TCU} over CAN bus), clock time and ifthe clutch is open or closed (supplied by clutch switch directly tovehicle monitor).

The vehicle monitor takes this information and accumulates the amountenergy produced by the engine (kilowatt-hours as described above) for atotal transmission energy measurement. The vehicle monitor alsomaintains a separate accumulator for engine energy put into thetransmission for each transmission gear range.

For example, to gain an accurate understanding of clutch duty thevehicle monitor may measure the total energy deposited in the clutchduring engagement and disengagement actions. The amount of energydeposited in the clutch is the amount of power lost in the clutchmultiplied by the time interval of the observation: (input power−outputpower)*time interval.

This is proportional to (input speed−output speed)*torque*time interval(Here's why: input power=input speed*input torque, output power=outputspeed*output torque, torque is the same on input and output thereforepower lost in the clutch is proportional to the speed difference acrossthe clutch times torque).

The input speed to the clutch is engine speed as supplied by the ECU viaCAN bus.

The output speed of the clutch is derived from the road speed of thevehicle adjusted for tire circumference, final drive (axle) ratio, andtransmission ratio for the current gear. Output speed (rev/min)=Roadspeed (mile/hour)*tire circumference (rev/mile)*hour/60 minutes*finaldrive ratio*transmission ratio for current gear

For example, for suspension components subject to cyclic variations inload, but not force reversals, the vehicle monitor may accumulate thenumber of and magnitude of force fluctuations in the suspension. Thevehicle monitor may first calculate the static force on the suspensionfrom an estimate of vehicle mass supported by the suspension and thenadd to this the force of upwards accelerations of the vehicle asmeasured by an accelerometer.

This accelerometer responds in a positive sense to vehicle upwardsaccelerations due to road roughness. These accelerations, times thesupported mass, is the additional force on the suspension due to roadroughness (F=ma).

The vehicle monitor accumulates a count of fluctuations, bucketed intosmall ranges of force, for every force fluctuation observed in thesuspension. The resulting histogram of number of events at a given forcelevel is an indicator of suspension duty. (Later the vehicle monitor maywill use this data, an S-N curve of the component, and Miner's Rule topredict life of components subject to this duty.)

For determining engine efficiency, the vehicle monitor may need toaccumulate an engine “time at speed vs. torque map.” In this map, thevehicle monitor may accumulate the amount of time of engine operation atevery engine speed and engine torque (bucketed into small bands of speedand torque). This data is used in conjunction with an engine BSFC map(described below) to estimate fuel consumption of differentengines/driveline combinations. The engine speed and torque are suppliedby the vehicle's ECU.

As indicated above, the vehicle monitor collects data used to measurethe durability and efficiency of the components available for use inbuilding a vehicle. Using measures of duty as described above, andknowledge of vehicle component wear state or failure state orefficiency, the vehicle monitor forms estimates of each component ofinterest's durability or efficiency under known, measured usage. Thisdata is then used to form a model of each component of interest'sdurability under three different failure or wear out mechanisms: cyclicfatigue failure, ultimate strength (bending) failure, and frictional ortribological wear. Depending on the component, most component failure orwear-out can be usefully described by one of these three patterns. Thedata is also used to form a model of each component's efficiency underall operating conditions.

Cyclic fatigue failure occurs when a component has been under repeatedcycles of stress less than the stress to produce permanent bending. Asthe number of cycles increases, the part will eventually fail,especially at higher stress levels. This phenomenon is shown graphicallyin an S-N curve that depicts the number of cycles possible at a givenstress level before a failure occurs.

Cyclic fatigue failures commonly occur in suspension components aftermany miles of driving on rough roads, which create large repeatedstresses on the components.

Overall suspension cyclic loading can be measured using a number ofdifferent sensors: a chassis-mounted accelerometer to calculate overallsuspension-driven forces on the chassis, an air-suspension heightmeasurement to show suspension displacements driven by road roughness,air-suspension pressure measurements to help estimate suspension staticloading, wheel angular displacement sensors (ABS sensor, toothed wheeland magnetic sensor) to show instantaneous wheel accelerations due toroad roughness.

Ultimate strength failures occur when a component is stressed to thepoint that it bends permanently. These failures commonly occur when acomponent is overloaded or operating at normal load levels in anoverheated state. Ultimate strength failures commonly occur whencomponents are subject overloads such as hitting a pothole in the road.For suspension components, the sensors may be the same as thosemeasuring cyclic fatigue (above).

Frictional or tribological wear is related to components in sliding orrolling contact. The amount of wear is related to the force of contact,lubrication state of the contact, temperature of the contact and time ofthe contact.

By measuring these parameters, the vehicle monitor can form an estimateof frictional wear or tribological wear. Frictional or tribological wearcommonly occurs in brakes and clutches (frictional wear) or bearings andgears (tribological wear). In these cases, the amount of energydissipated or transmitted by the component is an indicator or wear.Sensors for measuring energy dissipated in a system are described abovein the brake duty and clutch duty descriptions. Engine energy productionis measured using ECU-supplied torque, power and engine speed.

The vehicle monitor may estimate the engine efficiency in a particularapplication.

The vehicle monitor may measure the engine's Brake-Specific FuelConsumption (BSFC) at every engine speed and engine torque level in theengine's operating range. The vehicle monitor may observe the engine'sfuel consumption as supplied by the ECU, the engine's torque level assupplied by the ECU and the engine's speed as supplied by the ECU.

This data is stored in a two-dimensional map as shown in FIG. 4.

The x-axis shows engine speed. The y-axis shows engine torque. Thecoloring and contour lines show the brake power specific fuelconsumption (grams/hour per kilowatt of power (g/kw·hr). This map whencombined with the actual duty of the vehicle can estimate the vehicle'sfuel consumption for the specified duty.

This is done by using the time-at-speed-and-torque map described aboveto determine time and then determining the fuel consumption rate at eachtorque and speed point by lookup in the BSFC map.

Then, by multiplying time by fuel consumption rate, the vehicle monitormay get total fuel consumed at every speed and torque point (time(hr)*fuel rate (gr/hr)). The vehicle monitor may then add up the fuelconsumed at each speed and torque point to determine the total fuelconsumed for the given duty.

Overall vehicle performance can also be estimated with MTBF, repairrates and other measures given a known duty cycle.

For components that are subject to cyclic fatigue failure an S-N curveis formed from field data of failed components. The S-N curves of manysimilar failed components are averaged together to form a cyclic fatiguemodel for that component. The S-N curve is then used with a lineartheory of accumulated damage (Miner's Rule) to estimate the durabilityof the component given the measured duty cycle.

For components that are subject to ultimate strength failure (bending oroverloading, described above) field data is used to determine themaximum loading that a given component is subjected to. For example, toevaluate suspension-loading, vehicle mounted accelerometers can measurevertical accelerations of the vehicle related to rough roads or roadswith uneven surfaces. The vehicle's axle and suspension system transmitsforces from the roadway surface to the mass of the vehicle.

The magnitude of the vertical accelerations is proportional to theforces needed to create the accelerations (F=ma). Forces to create thevertical accelerations of the vehicle are added to forces needed tosupport the weight of the vehicle (mass of vehicle times acceleration ofgravity) to form a suspension loading profile. The peak forcestransmitted by the axle and suspension system can then be determined bythe peak accelerations observed in the field data.

For components that are subjected to frictional wear or tribologicalwear, a measure of the total energy dissipated in the system (brakes andclutches) or transmitted by the system (gears and bearings) along withknown, measured wear is used to form an energy-based wear-out measure.For example, using the accumulated brake energy measure mentioned abovemade on worn out brake components indicates the energy the brakes arecapable of dissipating before wear out.

The remote computer and/or the vehicle computer may analyze data fromthe following sources:

a. OEM system and sub-system durability data or usable life estimatesgenerated by engineering analysis and laboratory testing.b. Vehicle monitor vendor supplied, field empirical, system andsub-system durability data or usable life estimates generated bycomparing wear-out and repair history to vehicle duty (as describedabove),c. A list of plausible vehicle configurations or vehicle configurationsacceptable to the customer.

The above are used produce a vehicle system performance model and testeach particular configuration for performance against goals.

The vehicle system performance model that describes a componentwear-out, component failure, system efficiency or other parameter ofinterest is run with the measured duty of the application to produceratings of the different configurations.

For example: the measured duty is used with an S—N based fatigue modeland the theory of accumulated damage to estimate the durability ofcomponents of interest in each vehicle configuration. These durabilityestimates are compared to OEM and customer needs (e.g. projectedwarranty cost, projected down time, projected fuel efficiency, etc.) todetermine which configurations meet OEM application durability needs andcustomer cost targets.

The theory of accumulated damage uses an S-N curve to estimate thenumber of cycles possible at any given level of stress. When a componentis subject to a given level of stress, for which N cycles are possible,the vehicle monitor may say that 1/N of the life of the component isused up. If the vehicle monitor may keep track of each stress cycle thecomponent undergoes, the vehicle monitor can tally up how much componentlife is used in each cycle. At some point in the component's life, thetally will indicate that all of the component's life is used up. At thispoint, the vehicle monitor may recommend replacing or overhauling thecomponent.

When the number of plausible vehicle configurations is large enough thatcomputing ratings for each configuration is not possible a goal seekingand optimization of configurations is accomplished in any of a number ofdifferent ways:

a. Manual modification of configurations, where the user is in the loopchanging configurations to seek better results,b. Automated modification of configurations using Monte Carlo, partialderivative optimization or other techniques to seek better results.c. Massive parallel analysis of all possible configurations andsubsequent sorting/searching for optimal configurations.

FIG. 5 illustrates an example of a vehicle monitor 310.

Vehicle monitor 310 includes processor 313, memory unit 312, powermanagement and supply unit 329, modem and GPS unit 328 and various inputoutput units such as one or more CANBUS transceivers 314, one or moreJ1708/RS485 transceivers 315, one or more K-Line/ISO9141 transceivers316, one or more RS232 transceivers 318, 1-wire driver and keypad/buzzerpower unit 317, digital inputs and VSS+ignition unit 327, analog inputs326, digital outputs/open collectors 325, extension card 320 and auxport 321.

The various input output units are merely examples of various types ofconnections/links between the processor 313 to other units (such assensors 340, 342 and 344 of FIG. 3 and/or controllers 330 of FIG. 3).

Memory unit 312 may be a non-volatile memory unit and/or a combinationof volatile and non-volatile memory units.

The vehicle monitor 310 may include multiple processors—especially whenthe vehicle monitor 310 is configured to calculate the performances ofthe vehicle.

FIG. 6 illustrates method 500 according to an embodiment of theinvention.

Method 500 is for evaluating a performance of a vehicle when the vehicleis operated according to a given application.

Method 500 may start by step 510 of sensing sensed vehicle parameters bymultiple vehicle sensors that may include multiple types of sensors. Thesensors may be read by multiple means, as analog voltages, digitalvalues or network data packets. The sensors include those commonlyfitted to the vehicle such as, a measure of time, engine oil pressure,coolant temperature, wheel angular rotating displacement and tirepressure.

Step 510 may be followed by step 520 of determining, by a vehiclemonitor, based on the sensed vehicle parameters, duty related parametersof multiple vehicle components. The vehicle monitor is mechanicallycoupled to the vehicle or installed in the vehicle. For example, theamount of time the vehicle is operated on rough roads and the magnitudeof that road roughness determine the duty of many different componentson the vehicle including tires, axles, suspension linkages, suspensionbushings and suspension springs.

Step 520 may include at least one of the following:

a. Determining a stress cycle histogram of a vehicle component.b. Estimating an energy dissipated by one or more vehicle components.c. Estimating an energy dissipated by a vehicle brake during a brakingprocess by multiplying a calculated or measured mass of the vehicle by asum of (i) a change of vehicle speed during the breaking process and(ii) a change of vehicle altitude during the breaking process.d. Measuring a distribution of engine power over time.e. Measuring a distribution of transmission transmitted power over time.f. Measuring a distribution of power dissipated by the clutch over time.g. Adding a static force applied on a suspension of the vehicle toestimated dynamic forces applied on the suspension during movement ofthe vehicle.

Step 520 may be followed by step 540 of calculating the performance ofthe vehicle when operating according to the given vehicle configuration.

Step 540 may include calculating the performance of the vehicle basedon, at least, (i) the duty related parameters of the multiple vehiclecomponents and (ii) relationships between the duty related parameters ofthe multiple vehicle components and the performance of the vehicle. Forexample, vehicle fuel consumption for a particular vehicle configurationand driving duty is calculated by combining the engine's measured brakespecific fuel consumption, the driveline's (transmission and axle) gearratio configurations and the measured duty of the vehicle.

Step 540 may include at least one of the following:

a. Calculating performance parameters of the multiple vehiclecomponents. For example—calculating the durability and/or efficiency ofmultiple vehicle components.b. Calculating the performance of the vehicle based on the performanceparameters of the multiple vehicle components.c. Calculating a durability of each of the multiple vehicle components.d. Calculating a durability of at least one vehicle component based on acyclic fatigue failure of the at least one vehicle component.e. Calculating a durability of at least one vehicle component based onan ultimate strength failure of the at least one vehicle component.f. Calculating a durability of at least one vehicle component based onfrictional wear or tribological wear of the at least one vehiclecomponent.g. Calculating performance parameters of the vehicle systems.

Steps 510, 520 and 540 may be applied while the vehicle is configuredaccording to a certain configuration and step 540 may reflect theperformance of the vehicle if of the given configuration and when thevehicle is operated according to a given application.

There may be a need to evaluate the performance of the vehicle when thevehicle is operated according to the given application but is configuredin multiple configurations.

Duty related parameters of the multiple vehicle components (when thevehicle is configured to another configuration) may be measured onanother vehicle, may be measured by the vehicle during a period in whichthe vehicle was configured according to other configuration and wasoperated according to the given application.

Additionally or alternatively, duty related parameters may be deductedbased on information provided by the OEM, by clients of the OEM, by andthe like.

Accordingly—step 540 can be executed multiple times—once per eachconfiguration of the multiple applications.

After the evaluation of the vehicle performance at different vehicleconfigurations (and according to the application of interest) the methodmay select a selected vehicle configuration. The selected vehicleconfiguration can be, for example, the cheapest vehicle configurationthat can withstand the application of interest. Any other selectionparameter may be used.

FIG. 7 illustrates method 502 according to an embodiment of theinvention.

Method 502 is for evaluating a performance of a vehicle when the vehicleis operated according to a given application.

Method 502 may start by step 510 of sensing sensed vehicle parameters bymultiple vehicle sensors that may include multiple types of sensors.

Step 510 may be followed by step 520 of determining, by a vehiclemonitor, based on the sensed vehicle parameters, duty related parametersof multiple vehicle components. The vehicle monitor is mechanicallycoupled to the vehicle or installed in the vehicle.

Step 520 may be followed by step 530 of transmitting, by a vehicletransmitter, the duty related parameters of the multiple vehiclecomponents.

The vehicle transmitter may be included in the vehicle monitor, may becoupled to the vehicle monitor, may be included in a communication unit,and the like.

The aggregate size of the duty related parameters of the multiplevehicle components may be less than one thousandth of an aggregate sizeof the sensed vehicle parameters.

The aggregate size of the duty related parameters of the multiplevehicle components is a fraction (for example less than 1/100 or 1/1000or 1/10000 of 1/1,000,000) of the aggregate size of the sensed vehicleparameters.

Step 530 may be followed by step 535 of receiving the duty relatedparameters of the multiple vehicle components by a remote computer.

Step 535 may be followed by step 540 of calculating the performance ofthe vehicle when operating according configured according to the givenvehicle configuration.

Step 540 may be executed by the remote computer.

There may be a need to evaluate the performance of the vehicle when thevehicle is operated according to the given application but is configuredin multiple configurations.

Duty related parameters of the multiple vehicle components (when thevehicle is configured to another configuration) may be measured onanother vehicle, may be measured by the vehicle during a period in whichthe vehicle was configured according to other configuration and wasoperated according to the given application.

Additionally or alternatively, duty related parameters may be deductedbased on information provided by the OEM, by clients of the OEM, by andthe like.

Accordingly—step 540 can be executed multiple times—once per eachconfiguration of the multiple applications.

After the evaluation of the vehicle performance at different vehicleconfigurations (and according to the application of interest) the methodmay select a selected vehicle configuration. The selected vehicleconfiguration can be, for example, the cheapest vehicle configurationthat can withstand the application of interest. Any other selectionparameter may be used.

FIG. 8 illustrates method 600 according to an embodiment of theinvention.

Method 600 may be used for selecting a selected vehicle configuration.

Method 600 may start by step 610 of calculating for each vehicleconfiguration out of a group of vehicle configurations a performance ofa vehicle when operating according to a given application and configuredaccording to the vehicle configuration.

Step 610 may be followed by step 620 of selecting the selectedconfiguration out of the group of vehicle configuration. Any selectioncriteria may be used.

Step 610 may include executing steps 510, 520, 530 and 540.

Duty related parameters of the multiple vehicle components (when thevehicle is configured to another configuration) may be measured onanother vehicle, may be measured by the vehicle during a period in whichthe vehicle was configured according to other configuration and wasoperated according to the given application.

Additionally or alternatively, duty related parameters may be deductedbased on information provided by the OEM, by clients of the OEM, by andthe like.

Any one of method 500, 502 and 600 may include determining, by thevehicle monitor, based on the sensed vehicle parameters, efficiencyrelated parameters of at least one vehicle components. This may beexecuted in addition to or instead of step 520.

FIG. 9 illustrates vehicle monitor 700 according to an embodiment of theinvention.

Vehicle monitor 700 include a real-time processor 710, an applicationprocessor 720, first memory unit 712, second memory unit 722, cybersecurity module 732, multiple communication units/interfacing units suchas vehicle monitor to vehicle communication unit 730, wirelesscommunication unit 734 and input output unit 702.

The vehicle monitor 700 may include and/or may be coupled to sensorssuch as video cameras 742, accelerators and the like.

The input output unit 702 may be coupled to various sensors via busessuch as analog buses, digital buses, may be coupled to relays, and thelike. Non-limiting examples of various communication protocols that aresupported by the input output unit 702 and/or buses may include RS232,RE485, J1708, CANBUS, K-line, HAMI, VGS, IBIS and AV and/or DC feeds.

The wireless communication unit 734 may wirelessly communicate using anywireless protocols such as but not limited to Wi-Fi, Bluetooth, BLE, 3Gcellular, 4G cellular, 433 Mhz RF link, LORA.

Vehicle monitor to vehicle communication unit 730 interfaces between thevehicle monitor and vehicle systems, vehicle sensors vehicle systemcontrollers and the like. Non-limiting protocols and/or buses supportedinclude CANBUS, OBD, J1708 and LIN.

Cyber security module 732 mitigates cyber attacks and may be coupledbetween the wireless communication unit 734, the real-time processor710, the application processor 720 and the vehicle monitor to vehiclecommunication unit 730.

The real-time processor 710 and the application processor 720 can be anyhardware processors such as but not limited to CORTEX-A7 and CORTEX-M4of Qualcomm Inc.

The first memory unit 712 is coupled to real time processor 710. Secondmemory unit 722 is coupled to application processor 720.

Real-time processor 710 may process data from sensors in real time whileapplication processor 720 may execute more complex tasks (that may beless urgent) such as determining an actual wear of a vehicle, and thelike.

Faster Product Improvement

In heavy trucks, warranty failures and diagnostic trouble codes (DTCs)drive most of today's product improvement efforts.

OEMs current processes are driven by field failure rates and onboardDiagnostic Trouble Codes (DTCs). The OEMs current product improvementmethod is driven by feedback from the field after failures occur.

In an improvement method with feedback driven by failures, truck usersalways suffer the inconvenience and economic loss associated with fieldfailures before problems are fixed. In the new improvement method,feedback on component and system performance is obtained and analyzed inadvance of failures. This feedback is based on detailed observation ofcomponent performance and analysis of component performance performed ona monitor system in the vehicle. By performing the analysis in thevehicle, we avoid the cost of moving large amounts of data fromobservations across a wireless network. Only the high-level summaries ofcomponent performance are moved across the network. This system has thefurther advantage of offering immediate, real-time alerts to the drivershould a component be near failure because time to transport data acrossthe network is eliminated.

There is provided a system, method and computer readable medium thatstores instructions that cost effectively observes system performance inall or most of a large production run of trucks and predicts failuresand DTCs long before they happen. Truck OEMs can then initiate productimprovement actions before failures occur.

The method works by making accurate online measurements of trucksystems' performance and then using these measurements to learn systemperformance trends.

The combination (e.g. average) of trends from many similar trucks overtime form a background that illustrates the normal performance of thetruck in field use. For a particular truck, at particular time, when theobserved trend varies significantly from background or expected trendsor otherwise indicates an impending failure, the system sends anotification for further investigation.

Expected, normal operating range, performance trends are generated fromStatistical Process Control (SPC) type techniques based on engineeringmodels from both analytic (engineering equations) and empirical sources(on-the-road data gathered by a telematics system). This techniqueinvolves observing a particular parameter of interest (e.g. coolanttemperature, engine Brake Specific Fuel Consumption) and usingstatistical correlations to explain variation of the parameter usingother parameters related to the truck or its operating condition (e.g.ambient air temperature, truck speed, truck mass). Next create a modelof the observed parameter of interest with the effects of otherparameters removed. This model is then used with data from many trucksin normal operation to determine the amount of unexplained variation inthe model.

For example, a model describing engine coolant temperature accounts fora number of factors that change the coolant temperature duringoperation. These factors include ambient air temperature, engine rpms,engine power, vehicle speed and radiator fan speed (if driven separatelyfrom the engine speed). The model is formed using field observations ofthe coolant temperature and the other factors mentioned above measuredat the same time. For a normally operating truck, we use a multiplelinear regression model to estimate the influence of each factor (inputvariables) on the coolant temperature (response variable). We thencalculate the standard deviation of the model's output of coolanttemperature with respect to the measured field data. The standarddeviation describes the amount of unexplained variation in the model.

For trucks in operation, we will often set the allowable differencebetween model output and measured coolant temperature at 2 times thestandard deviation of the model. This means that, for normallydistributed model errors, the measured value is within the threshold 95%of the time.

The model output and the amount of unexplained variation in the modeloutput for normal truck operation form the basis of comparison to trucksoperating in the field (this is the normal operating range of theparameter). For a truck in the field, when the model of the observedparameter of interest exceeds the normal unexplained variation level,the truck is flagged for further investigation as to why the modeledparameter exceeded normal unexplained variation. This reason is often asystem or component performance degradation or failure.

In some cases, the duty a system is subjected to influences theperformance of the system.

In these cases, the duty cycle is measured and used as an input to thesystem performance trend analysis. For example, trucks subjected toheavy loading will wear out at a higher rate than trucks subjected tolight loads. The loading duty cycle will be an important factor indetermining the variation in many truck parameters.

The online measurement of the truck systems performance may involvedirect measurements of easily interpreted parameters and may alsoinclude performance measures based on combinations of measuredparameters used to infer system performance.

Online access and a help-desk type facility gives users responsible forproduct improvement access to automatically generated flags andunderlying data. For example, an online control chart of the parameterof interest displayed with other vehicle operating parameters.

In some cases, the diagnostic system and/or the electronic controls ofthe truck system under observation may be reprogrammed while in use(OTA—Over the Air reprogramming). This is done with a secure wirelessdownload capability. In use reprogramming, may help the diagnosticsystem home in on a problem or may help resolve a field issue. Forexample, because components can fail in many different ways and that itis not possible to fit computer code for every type of failure in amobile computing platform we provide for dynamically loadable analysiscode packages. When the overall system monitor detects that particularcomponent is trending away from normal operation, we send an analysiscode package to the in-vehicle monitor that specifically targetsanalyzing data for the component in question. The goal of this operationis not just to identify that the component is trending away from normaloperation, but also to make a more detailed analysis to determine why(root-cause) the component is trending this way.

FIG. 10 illustrates method 1100 according to an embodiment of theinvention.

Method 1100 may start by step 1101 of measuring truck operatingparameters using a vehicle monitor (such as a telematics system—such asbut not limited to Traffilog Multi-Bus Unit telematics system).

The parameters may include, for example:

a. Measured values (e.g. vehicle speed, engine coolant temperature,pressures, etc.),b. Filtered values—measured values averaged or filtered over a period oftime,c. Calculated values—mathematical combinations of other values (e.g.speed is a combination of distance traveled divided by the time totravel that distance, engine efficiency is a combination of measuredengine power output and measured engine fuel consumption),d. Inferred values—values created by inferring the internal state of asystem using the above values and a system model (e.g. brake remaininguseful life is a combination of all brake usage information and a brakewear-out model).

Step 1101 may be followed by step 1102 of comparing parameters to normaloperating range targets.

Normal operating range targets may be set, for example, using one ormore of the following techniques:

a. Simple thresholds—operating range target that does not change overtime.b. Dynamic thresholds—operating range target that changes as a functionof time or as a function of another parameter (e.g. engine load, totalbrake energy dissipated, etc.).c. Configuration specific thresholds—operating range targets that areinfluenced by the truck's particular configuration (e.g. engine size,transmission ratios).d. Calculated thresholds—operating range targets that are a function ofa system model or other calculation (e.g. brake remaining useful life,catalyst temperature).e. Population specific thresholds—operating range targets that are afunction of a population's observed performance (e.g. mean plus or minus1 standard deviation to set threshold).f. Rate specific thresholds—thresholds set in part by the rate at whichthe parameter of interest is changing (related to a threshold for thederivative of the parameter of interest) (e.g. thresholds for exhaustgas temperature normal variability get wider when rate of change ofengine speed is large).g. Integrated specific threshold—thresholds set in part by theaccumulated value of the parameter over some period of time (related tothe integral of the parameter of interest) (e.g. thresholds for coolanttemperature normal variability get wider when cooling system operatinghours increases, change due to lower radiator capability with dirt andwear, lower coolant pump capability with pump and drive belt wear).

Normal operating range is expected to be between the upper threshold andlower threshold of the parameter. For example, if normal engineoperating temperature is 90 C the threshold may be +/−5 C for a newtruck and +/−7 C for a used truck.

If the parameters are within normal operating range, then step 1102 isfollowed by step 1101.

If the parameters are outside of normal operating range jumping to step1103.

Step 1103 may include initiate data handling actions appropriate for theparticular parameter outside of normal range.

Step 1103 may include, for example:

a. Sending a notification of the out of range parameter.b. Sending a snapshot of the conditions before, during or after the outof range circumstance.c. Initiating specialized data collection, data analysis or datatransmission to collect detailed information around the circumstances ofthe out of range condition.

Step 1103 may be followed by step 1101.

As a result of notifications listed above initiate system improvementactions necessary to avoid field failures and warranty costs.

Method 1100 may be executed by a computer or by a system that includes acomputer as well as information sources and/or sensors such as vehiclemonitors.

FIG. 11 illustrates method 1200 that includes steps 1201 through1206—that are one particular example of the process described by steps1101 through 1103.

Step 1201 creates tolerances and calibrations for an online observationprocess. The tolerances set the allowable range of observed operationbefore signaling a fault. The calibrations are used by algorithms in theobserver that can modify the tolerances depending on conditions in thevehicle. For example, the battery voltage during engine cranking is afunction of battery state of charge, number of charge/discharge cycleson the battery, starter current draw, and battery temperature. In atypical battery model, each of these terms have a factor that relatesthe parameter to battery voltage during cranking. Battery Voltage duringcrank=Base voltage+a*state of charge+b*number of cycles+c*startercurrent+d*battery temperature, where a, b, c, d are factors forparameters state of charge, number of cycles, starter current, batterytemperature respectively. This calibrated voltage is then the expectedvalue of the voltage measured on the truck plus or minus the suppliedtolerance.

Step 1202 uses the calibrations to calculate and apply changes totolerances during testing. This allows the system to track changes inthe vehicle's operating condition and relate this to expected changes inmeasured parameters.

Step 1203 is an observation process that reads measurements of thesystem under test and compares those measurements to current tolerances.

Step 1204 analyzes measurements that are outside of the currenttolerances (called limit excursions) to determine if a problem is found.For example, a single out of tolerance value of a particular measurementmay not indicate a problem. By contrast, a series of out of tolerancevalues of a particular measurement may indicate a problem.

Step 1205 branches to 1206 if a problem is found or branches to 1202 ifno problem is found.

Step 1206 notifies responsible parties that a problem was found. Theresponsible parties would then initiate changes to the product or testsystem to address the problem. The product may be modified in the fieldby Over the Air (OTA) reprogramming of electronic control modules. Thetest system may be modified in the field by OTA reprogramming of thesoftware in the field test system.

Methods 1100 and/or 1200 may be executed by a computer, a vehiclemonitor, and the like.

FIG. 12 illustrates method 1400 according to an embodiment of theinvention.

Method 1400 may start by step 1410 of measuring multiple vehicleoperating parameters using a vehicle monitor; wherein the vehiclemonitor is mechanically coupled to the vehicle or installed in thevehicle.

Examples of measuring vehicle parameters are provided in the sectionabove titled “ACCURATE APPLICATION APPROVAL”.

Step 1410 may be followed by step 1415 of storing by the vehiclemonitor, vehicle operating parameters that were obtained over a periodof time (such a few seconds, few minutes, and the like) to providestored vehicle operating parameters.

Step 1410 may be followed by step 1420 of searching, by the vehiclemonitor, for one or more out-of-range vehicle operating parameters;wherein an out-of-range vehicle operating parameter is a vehicleoperating parameter that is outside an allowable range of the vehicleoperation parameter.

The allowable range of a vehicle operation parameter may be fixed,dynamic (change over time and/or based on an event), may be timedependent, may be dependent upon a value of one or more other vehicleoperation parameters, may depend on the configuration of the vehicle,may depend on a rate of change of a value of the vehicle operationparameter and/or may depend upon an accumulated value of the vehicleoperation parameter over a period of time.

If not finding any out-of-range vehicle operating parameter, thenjumping to step 1410.

When finding out-of-range vehicle operating parameters then jumping tostep 1430 of responding to the one or more out-of-range vehicleoperating parameters by the vehicle monitor.

The one or more out-of-range vehicle parameters may be indicative of atleast one vehicle failure that is impending (still did not occur).

Step 1430 may precede the occurrence of the at least one vehiclefailure.

Step 1430 may include at least one of the following:

a. Sending one or more out-of-range alerts indicting about the one ormore out-of-range vehicle operating parameters. (Step 1431).b. Sending additional information relating to the more out-of-rangevehicle operating parameters. (Step 1432).c. Requesting to receive a vehicle monitor software update for managingthe one or more out-of-range vehicle operating parameters. (Step 1433).d. Triggering a vehicle monitor software update for managing the one ormore out-of-range vehicle operating parameters. (Step 1434).

Step 1431 may include sending the one or more out-of-range alerts to asystem that is external to the vehicle, and/or sending the one or moreout-of-range alerts to a vehicle system and/or generating one or morehuman perceived out-of-range alerts.

When an out-of-range parameter is detected, the monitoring systemoperation may be modified to collect more detailed information relatedto the out-of-range parameter. The operation is modified by updatingmonitoring software in real-time, during vehicle operation. The changein monitoring system operation may be as simple as collecting andtransmitting more raw data from the out-of-range system or as complex ascalculating more system state parameters from all sources of data on thevehicle. For example, when the engine coolant temperature goesout-of-range, we may capture more detailed information on the engine'soperating power, torque and speed and use this with a thermal efficiencymodel of the engine to determine cooling system load during the time ofout-of-range temperatures.

Referring to step 1432—the additional information may include aplurality of the stored vehicle operating parameters, and/or a snapshotof the at least some of the multiple vehicle operating parameters beforefinding the one or more out-of-range vehicle operating parameters,and/or a snapshot of the at least some of the multiple vehicle operatingparameters after finding the one or more out-of-range vehicle operatingparameters.

Step 1433 may be followed by step 1436 of receiving, by the vehiclemonitor (and maybe from a remote computer), the software update.

Step 1436 and 1434 may be followed by step 1437 of updating, by thevehicle monitor, the vehicle monitor software with the software update.

Step 1437 may be followed by step 1848 of managing, by the vehiclemonitor, the one or more out-of-range vehicle operating parameters.

Step 1848 may include at least one out of changing a configuration of avehicle component associated with the one or more out-of-range vehicleoperating parameters and changing a monitoring parameter associated withthe one or more out-of-range vehicle operating parameters. Themonitoring parameter may include a frequency of monitoring, theresolution of monitoring, the duration in which monitoring result arestored at the vehicle monitor and the like.

Faster New Feature Launch

Regulations for safety, fuel-efficiency and greenhouse gasses arechanging at an accelerating rate. Truck OEMs respond to new regulationswith new products that are more complex and need to be launched oncompressed schedules. Historically, all new product feature launchesinclude extensive laboratory testing and extensive field-testing. Thisinvention shortens the amount of time and reduces the cost to completethis testing.

In many cases, the OEMs initial laboratory testing and field-testing isnot adequate to insure a successful launch. This system helps byallowing field-testing of a larger population and by spotting problemsbefore they become failures. Also, the system can accurately measure andcharacterize the conditions under which the new component/system will beused.

There is provided a system, method and computer readable medium thatstores instructions that speeds up the testing of a features by allowingfield-testing of a larger population and by spotting problems beforethey become failures. Also the system can accurately measure andcharacterize the conditions under which the new component/system will beused.

There is provided a system, method and computer readable medium that mayassist OEMs in launching new systems and/or new features more quicklyand with higher confidence that the new system will work as intended.This is done by using cost-effective and bandwidth efficient telemetryto monitor new systems and subsystems in large-scale field-trials.

The terms “system” and “feature” are used in an interchangeable manner.Although the following examples will refer to a new system (new trucksystem) they are applicable to a new feature (new vehicle feature).

Non-limiting examples of vehicle systems include engine, transmission,brakes/antilock brakes, electronic stability control, axle/driveline,suspension/ride height control, tires/tire inflation monitor/control,instrument panel/driver interface.

Non-limiting examples of features may include one or more components,sub-systems, and the like. For example—a new axel, a new controller, andthe like. The feature may be a mechanical feature and/or an electronicfeature.

The system may include vehicle monitors that may transmit over one ormore networks information to a computer. The system may utilizeTraffilog's “campaign” approach for collecting and processing fielddata, which is completely adaptable to different situations.

In this approach, trucks with the new systems (e.g. new engine, aftertreatment, brakes) are driven in large-scale (e.g. 100 to 1000 units)field trials with a vehicle monitors such as the Traffilog optimalobserver campaign running on the telematics platform.

FIG. 13 illustrates method 2100 according to an embodiment of theinvention.

Method 2100 include the following steps:

Step 2101 of installing new components or systems for evaluation on thefleet of trucks,

Step 2102 of installing any special instrumentation required forobserving the performance of the new components or systems. For example,on a truck fitted with a new drive axle, we may install an input shafttorque transducer to measure loading on gears and bearings related todriving torque. We may also install sensors to detect system failures(e.g. limit switches, software calculated limits on system performance).

Step 2103 of connecting the instrumentation to the Traffilog system,activate an initial check-out and calibration campaign to initialize thein-vehicle equipment.

Step 2103 may include:

a. Dynamically loaded campaign applet,b. Checks instrumentation connectivity and measurements during initialstartup and calibration process,c. Cues technicians to perform actions to allow system check out (e.g.start engine, apply brakes, etc.),d. Delivers system status (go, no-go) and diagnostic information asneeded,e. Signals to load main observer campaign on successful completion ofinitial check out.

Step 2104 of operating the fleet of vehicles in one or more of thefollowing manners:

a. Naturalistic driving with no special feedback to the driver or fleetmanager regarding the duty of the truck. In this case, the telematicssystem automatically collects the vehicle's operating condition and theperformance of the component under test.b. Semi-targeted driving with automatically generated specialinstructions to the driver when special driving conditions are requiredfor testing (e.g. request the driver to alter normal driving patternswhen environmental conditions allow testing additional conditions suchas air temperature extremes or low traction roadway conditions). In thiscase, the system automatically records the vehicle's operating conditionand compares this record to a list of required drive conditions. Basedon this comparison the system may request the driver to operate thetruck at a certain condition or perform a particular maneuver. Forexample, a test may require a certain amount of time with the engineoperating at a given speed. Until the “drive at engine speed” target ismet, the system maintains a prompt to remind the driver to hit thetarget speed, if possible.c. Targeted driving with a closely controlled driving schedule toproduce particular wear on components (e.g. fixed-route, fixed-loaddriving or test track driving). In this case, the system guides thedriver through the required test schedule, by automatically keepingtrack of test schedule steps as they are accomplished.

For semi-targeted or targeted driving both the fleet manager and driverwill have instructions from the system to assist in completing a testprotocol. These instructions can be delivered by a number of mechanismsincluding web sites, smart phone apps or in-vehicle displays. The systemmay also direct drivers of field trials to use alternate routes ormodify normal driving behavior (e.g. gear used on highway cruise) toaccomplish complete test coverage. Drivers can be directed to accomplisha test protocol using a number of methods. For example, drivers can begiven an online checklist of conditions to meet that is displayed on thevehicle's telematics system. The checklist indicates which conditionsneed to be met on a particular drive. As each condition is met it ischecked off the list.

Regarding step 2104, little or no feedback is needed for driving in acompletely naturalistic test case.

For semi-targeted or targeted driving both the fleet manager and driverwill have instructions from the system to assist in completing a testprotocol. These instructions can be delivered by a number of mechanismsincluding web sites, smart phone apps or in-vehicle displays. The systemmay also direct drivers of field trials to use alternate routes ormodify normal driving behavior (e.g. gear used on highway cruise) toaccomplish complete test coverage. Drivers can be directed to accomplisha test protocol using a number of methods. For example, drivers can begiven an online checklist of conditions to meet that is displayed on thevehicle's telematics system. The checklist indicates which conditionsneed to be met on a particular drive. As each condition is met it ischecked off the list.

To modify base vehicle behavior, the telematics system can intervene innormal operation or reprogram the required vehicle system to achieve thebehavior change. This is done with either CAN bus messages or analogsignals.

Step 2105 of modifying base truck behavior to emphasize use of thesystem under test (e.g. disable retarder to accumulate more use on thefoundation brakes). To modify base vehicle behavior, the telematicssystem can intervene in normal operation or reprogram the requiredvehicle system to achieve the behavior change. This is done with eitherCAN bus messages or analog signals.

Regarding step 2105:

a. Data reductions techniques such as averaging or histogramming areused to reduce the amount of data sent over cellular data connections.b. More sophisticated techniques such as optimal observers or modelbased observers can be used as part of the recording campaign.c. Because the Traffilog system is dynamically reprogrammable while inoperation, tests can be modified on-the-fly to account for variabilityof component/system performance or other factors.d. Analyze the conditions under which the component/system is subjectedto on the truck. Compare this with OEM engineering specifications foroperating conditions. Report any variances.

Step 2106 of recording driving duty on all trucks, record component andsystem performance on all trucks.

Regarding step 2106:

a. Data from the truck can be sent out on a periodic basis,b. Data from the truck can be sent out on as-needed basis triggered byconditions on the truck, requests from a central server or otherconditions,

Step 2107 of recording periodically report on accumulation of duty vs.component/system performance, collect driver, service technician orfleet manager comments.

Regarding step 2107:

a. Use techniques similar to “Faster Product Improvement” to detectvariances in component/system performance.b. Signal the driver or fleet manager as appropriate if immediate actionis needed.

Step 2108 of recording continuously monitor component and systemperformance for unexpected variations in performance (prognostics). Whenvariations are detected, capture the circumstances of the variance, senda signal to indicate a variance is detected, schedule inspections ormaintenance to learn the source of variance.

Step 2109 of collecting and analyzing periodic reports and variancesignals on back office infrastructure.

Regarding steps 2108 and 2109:

a. Use cellular infrastructure and internet data transport to get datafrom the truck to the back-office infrastructure.b. Normalize “as-driven” component/system performance back to “standardtest” conditions using any of a number of techniques such as, collectingthe number and severity of high-stress cycles and comparing tohigh-stress cycles on a standard test. The ability to use a large numberof devices under test with accurately measured test conditions allowstests to be completed more quickly with better test coverage.c. For cyclically stressed steel components, we can normalize measuredduty in the truck by comparing actual stress cycles in the truck (numberof cycles and stress levels) to known benchmark stress vs. cycle data.For other vehicle systems, such as brakes or clutches, a measure tototal energy dissipated is a good measure for comparing actual truckdata to benchmark data. For after treatment systems, accumulated time,mass flow, temperature, and chemical composition are good measures.d. Test results and analysis tools can be available online with helpdesk type support, which allows a large number of users (productengineers) to quickly use the system with a minimum of training.

Step 2110 of reporting, at the end of the test, on all aspects of truck,component/system performance.

Method 2100 may include sending an alert when the monitoring systemfails to start or run properly (self-test, watchdog functions).

The overall collection and analysis process may be useful incircumstances other than large-scale fleet tests.

For systems that require extreme test conditions that cannot be observedin normal field operation, tests can be accomplished in a laboratorycapable of generating the extreme conditions, but with fixturing andinstrumentation identical to that used in the fleet tests. This data isthen easily combined with field test data to form a complete picture ofthe system's performance. When over-stress data from a laboratory testuses the same instrumentation as is used on field trials, that data canbe treated as if it were generated on a truck in the field.

Field trial trucks may also carry multiple copies of a component orsubsystem that are monitored during truck operation, but not being usedonline for truck operation. For example, a single field test truck maycarry multiple ABS sensors mounted on an axle for vibration, shock andenvironmental testing although only one of the sensors is used in theonline ABS system. If multiple active copies of the component under testare possible, compare results from components on the same truck to findvariances. For example, temperature sensors should all read the sameunder cold soak conditions. Create events to capture and reportmismatched readings.

FIG. 14 illustrates method 2200 according to an embodiment of theinvention.

Method 2200 includes steps 2201-2210.

Step 2201 includes fitting new features, subsystems and systems on thevehicle to be tested.

Step 2202 involves running the vehicle under test in a field trialpossibly designed to stress the new feature, subsystem or system fittedin step 2201.

Step 2204 Sets expected values and tolerances of measured parametersrelated to the new feature, subsystem or system under test. These valuesdetermine if actions, alerts etc., are required during the field trial.

Step 2205 Sets expected failure levels of parameters related to the newfeature, subsystem or system under test. When a component is exposed tooperation at a failure level during a test an alert is generated even ifthe component's performance continues to be in range.

Step 2206 Sets tighter tolerances for in-range operation based on othertest results indicating a possible weak spot. For example, if one truckin a test fleet a cooling system problem, in-range tolerances forcoolant temperature may be changed for all trucks in the test fleet.

Step 2207 As a field trial progresses, and new or upgraded equipment isfitted to the vehicle, track these changes with corrected campaigntargets. As a field trial progresses and the lower level of componentsand sub-systems have proven reliable move the testing and observationtasks to higher levels of the system. For example, once the engineturbocharging, fueling and cooling systems have been observed to meettargets move the testing emphasis to overall engine power and efficiencytesting.

Step 2203 Based on campaign targets from multiple sources (steps2202—2207) observe the vehicle's behavior and compare to targets.

Step 1208 Analyze observed limit excursions and determine if a problemis found. In most cases, a limit excursion indicates a problem, which ispassed on to the next step. In some cases, we may apply additional logicto a limit excursion to reduce false problem indications. For instancemany parameters may be out of range during test conditions notanticipated in the models and thresholds used in the campaign. Forexample, a vehicle start during and extreme cold-weather may triggermany out of range parameters, which can be ignored.

Step 2209 If a problem is found branch to step 2210. If no problem isfound branch to step 2202.

Step 2210 Create new or modified features, subsystems and systems inresponse to problems found.

FIG. 15 illustrates method 2300 according to an embodiment of theinvention.

Method 2300 guarantees that the truck will undergo a set of operatingconditions that should be tested.

Method 2300 may include the steps of:

a. Step 2310 of operating a truck of a fleet of trucks.b. Step 2320 of monitoring the truck by a vehicle monitor.c. Step 2340 of comparing a set of truck operating conditions thatshould be tested to actual truck operation conditions that were observedduring the monitoring. The comparison is aimed to determine whetherthere is an untested truck operating condition that should have beentested.d. If finding an untested truck operating condition that should havebeen tested—then step 2340 is followed by step 2350 of requesting atruck driver to operate the truck according to the untested truckoperating condition that should have been tested.e. Step 2360 of determining that the driver operated the truck accordingto the untested truck operating condition that should have been tested.If the truck driver failed to operate the truck according to theuntested truck operating condition that should have been tested—then thetruck driver may receive notifications from the vehicle monitor and/or acontrol center or a third party may be notified about this problem. Ifthef. If the driver operated the truck according to the untested truckoperating condition that should have been tested—then step 2360 isfollowed by step 2370 of defining the (previously) untested truckoperating condition that should have been tested as a tested truckcondition.

Step 2370 may be followed by step 2340.

FIG. 16 illustrates method 2302 according to an embodiment of theinvention.

Method 2302 guarantees that the truck will undergo a set of operatingconditions that should be tested.

Method 2302 may include the steps of:

a. Step 2310 of operating a truck of a fleet of trucks.b. Step 2320 of monitoring the truck by a vehicle monitor.c. Step 2340 of comparing a set of truck operating conditions thatshould be tested to actual truck operation conditions that were observedduring the monitoring. The comparison is aimed to determine whetherthere are multiple untested truck operating conditions that should havebeen tested.d. If finding multiple untested truck operating conditions that shouldhave been tested-then step 2340 is followed by step 2342 of selectingand untested truck operating condition out of the multiple untestedtruck operating conditions. The selecting can be performed according toany selection criterion, in a ransom manner, in a pseudo random manner,according to a predefined order of actual truck operation conditions,according to estimated and/or actual conditions of a path to be followedby the truck (step 2344). The estimated and/or actual conditions may befor example, positive slopes, negative slopes, horizontal path segments,curvature of a path segment, roughness of a path segment, and the like.The path that is followed by the truck can be known in advance(predefined path dictated to the driver of the truck), can be a paththat is commonly used by one or more trucks of the fleet, and the like.The vehicle monitor can (based on characters of different paths segmentsof different paths) direct the truck driver to follow a certain paththat includes path segments in which different truck operatingconditions may be tested. The vehicle monitor may inform the driverbefore reaching a path segment which truck operating condition will betested on that path segment. The vehicle monitor may be fed with pathsegments conditions and/or may store truck information from previouspasses over the path segments and/or may receive information from othertrucks of the fleet that passed over the paths segments. For example—apath segment that has a steep positive slope may be used to test lowspeed high moment propagation while a path segment that has a steepnegative slope may be used to test the brakes of the vehicle and/orretarders.e. Step 2342 is followed by 2350 of requesting a truck driver to operatethe truck according to the untested truck operating condition thatshould have been tested.f. Step 2360 of determining that the driver operated the truck accordingto the untested truck operating condition that should have been tested.If the truck driver failed to operate the truck according to theuntested truck operating condition that should have been tested—then thetruck driver may receive notifications from the vehicle monitor and/or acontrol center or a third party may be notified about this problem.g. If the driver operated the truck according to the untested truckoperating condition that should have been tested—then step 2360 isfollowed by step 2370 of defining the (previously) untested truckoperating condition that should have been tested as a tested truckcondition.

Step 2370 may be followed by step 2342.

FIG. 17 illustrates method 2304 according to an embodiment of theinvention.

Method 2304 guarantees that the truck will undergo a set of operatingconditions that should be tested.

a. Step 2310 of operating a truck of a fleet of trucks.b. Step 2320 of monitoring the truck by a vehicle monitor.c. Step 2380 of guiding a driver to follow a test schedule thatdetermines a set of truck operating conditions that should be tested.The guiding may include instructing the drive to operate the truckaccording to one truck operating conditions that should be tested at atime.d. Step 2385 of determining that the driver followed the test schedule.If the truck driver failed to operate the truck according to the testschedule—then an alert may be generated (2390), the truck driver mayreceive notifications from the vehicle monitor and/or a control centeror a third party may be notified about this problem.

Real Time Failure Analysis and Accurate Warranty Claim Assesment

On commercial vehicles, failed components and systems and subsequentwarranty claims lead to customer dissatisfaction and costs for originalequipment manufacturers (OEMs).

OEMs currently have little evidence other than a failed part and/or aDTC on which to judge the validity of a warranty claim. This lack ofevidence leads to difficult relations between customers and OEMs.

The lack of evidence leads to difficult relations between customers andOEMs. Both parties are tempted to interpret the available evidence inthe differently. The lack of evidence rarely leads to an optimalsolution for both parties

There is a growing need to provide a method and system that will enableto provide thorough understanding the cause of failures and subsequentwarranty claims, OEMs can reduce unnecessary warranty claims and guidecustomers to products, usage processes and maintenance processes thatwill eliminate failures. Other interested parties include financingentities, suppliers, and fleets for a truck “health state” measure.

There is provided a system, method and computer readable medium thatstores instructions that efficiently measures the duty cycle of thetruck. In particular, the system measures load or force excursions thatcan cause disproportionately large damage to truck mechanical systems.With these measurements, both customers and OEMs can judge a particulartruck's performance against warranted performance and durability.

Every system on a truck has a predicted life under “normal” operatingconditions. When normal conditions are exceeded, OEMs typically try todeny warranty claims when failures occur. Getting to the root cause of afailure allows OEMs and customers to negotiate more accurately who paysand how to avoid failures in the future.

However, “normal” is difficult to define in detail without data. Byhaving data showing actual use, both OEMs and customers can agree on“normal.”

FIG. 18 illustrates method 3100 according to an embodiment of theinvention.

Method 3100 include the following steps:

Step 3101 of measuring the truck's actual duty using techniques such asthose described in the sections above titled “Faster ProductImprovement,” and/or “Faster New Feature Launch.”

Step 3101 may be followed by step 3120 of automatically alert drivers,fleet managers and/or OEMs to truck usage that exceeds warrantableoperation. For example, trucks operated in overloaded conditions areflagged immediately by the on-truck telematics unit. This unitcalculates the vehicle mass from a number of quantities measured on thevehicle (e.g. ride height and air-suspension inflation pressure).

In another example, trucks that are specified for freeway usage with amaximum sustained grade of +/−3% are flagged as out of warranty coveragewhen operation in state highway conditions +/−6% are detected.

The determination of time on grade can be measured directly using a3-axis accelerometer and a speedometer on the vehicle (when the roadspeed is not changing the average composite force vector from theaccelerometer will have magnitude of 1G and indicate the grade that thetruck is on, averaging removes the effect of bumps in the road). Time ongrade can also be determined from GPS position and a lookup on atopographical map to determine roadway grade.

Step 3101 may be followed by step 3102 of accumulating and storing theduty of the truck as measured in step 3101.

Step 3102 may be followed by steps 3103 and 3104.

Step 3103 may include applying methods and reports available for commonwarranty claims (e.g. brakes, tires, etc.). Apply data from externalwarranty claim system to understand what are the top warranty items.Apply data from external warranty claim system to better understand therelationship between truck diagnosis and warranty claims.

Step 3103 may include having predefined (even standard) analysis methodsand reports available for common warranty claims (e.g. brakes, tires,etc.) that are automatically populated by measured data from the truckin question. For example, in understanding tire failures, TMC RP-216Bdescribes the industry standard classifications for tire failures, whichcan be the basis for classifying failures. The classified failures inconjunction with a detailed understanding of the truck's actual duty(step 2, above) can lead to understanding the correlation between aparticular pattern of truck duty and tire failures. This is done bylooking for a statistical correlation between measured parametersdescribing the truck and/or the truck's duty and a particular type offailure.

Step 3104 may include applying data analysis tools to query a giventruck's history for less common warranty problems. Include an open APIfor interested parties to create apps or query a truck health database.Apply data from external warranty claim system to understand what arethe top warranty items. Apply data from external warranty claim systemto better understand the relationship between truck diagnosis andwarranty claims.

Steps 3103 and 3104 may be followed by step 3105 of providing a warrantyrecommendation and/or insuring a truck upon the outcome of steps 3103and 3104.

Many warranty claims arise from abuse of the truck due to overloading oroperation not included in the truck's application approval (e.g.excessive grade, unpaved roads).

Referring to step 3103—the information may be provided in one of thefollowing formats:

A standard report related to overloading might include: Average ladenGCW, Maximum operating GCW observed, Hours of operation over rated GCW

A standard report related to operation outside of application approvedlimits might include: Ton-miles of operation in non-approved conditions,Mileage of operation on rough roads and Mileage of operation onexcessive grades.

Other abuse conditions may include over speeding of engine, driveline orboth, over heating due to overload, over pressure of oil on cold start,lack of maintenance, overload of compressor due to extra trailers orleaks, under inflation of tires, clutch abuse, gearbox forced shifts,missed or inappropriate DPF regeneration.

Truck “wear state” or “health state” estimation can be used for

a. Finance/Portfolio value measures,b. Value measure of assets under finance,c. Actual value of capital assets (not estimated or modeled, e.g.residual values)d. Regulators/Safety Inspectors—interest in emissions compliance orsafety,e. Industry consortium—understand truck performance,f. Aftermarket and parts suppliers—interest in component performance,g. Government—Tax and toll collection purposes.

This insurance system is robust against outages that may compromise theintegrity or coverage of data from a given truck.

Ultimately, the execution of method 3100 may lead to new business modelswhere customers pay for the use of a truck based on the duty cycle ofthe mission where OEMs can influence the choice of truck for the mission(power by the hour). Other new business models include: insurance-likemodel, variable/extended warranty offering, warranty deletion option,financing options based on usage, and a discount for using the onlinesystem.

Having evidence of a truck's actual usage to determine the root cause ofa failure (e.g. OEM related flaw or customer abuse of truck).

By having good root cause determination of failures, customers and OEMscan work to optimize truck cost and performance, which saves money forboth parties.

FIG. 19 illustrates method 4000 according to an embodiment of theinvention.

Method 4000 may be used for failure analysis and warranty claimassessment.

Method 4000 may include:

Step 4010 of sensing sensed vehicle parameters by multiple vehiclesensors that comprise multiple types of sensors.

Step 4020 of calculating, by a vehicle monitor, based on the sensedvehicle parameters, parameters of multiple vehicle components; whereinthe vehicle monitor is mechanically coupled to the vehicle or installedin the vehicle. The sensed vehicle parameters may be duty relatedparameters and method 400 may include determining by the vehiclemonitor, based on the parameters of multiple vehicle components, anactual wear of the vehicle.

Step 4030 of searching in real time, and based on the parameters ofmultiple vehicle components for actual vehicle failures and/or forimpeding truck failures (truck failures that did not occur but areexpected to occur).

If an actual vehicle failure and/or for impeding truck failure isdetected then step 4030 may be followed by step 4035 of responding tothe vehicle failure. Step 4035 may include at least one of thefollowing:

a. Generating, by the vehicle monitor and in real time, an alert aboutthe vehicle failure.b. Generating by the vehicle monitor and in real time an alert about anexpected vehicle failure before the vehicle failure occurs.c. Instructing the vehicle, by the vehicle monitor, to change a vehicleparameter if the vehicle monitor determines that a vehicle failureoccurs or if a vehicle failure is impeding.d. Informing, when the truck failure is a truck system failure and bythe vehicle monitor, a vehicle controller that is responsible to thevehicle system about the vehicle system failure.

Step 4040 of determining, by the vehicle monitor and based on theparameters of the multiple vehicle components and by the vehiclemonitor, whether the operation of the truck exceeds a warrantableoperation of the truck.

Step 4050 of evaluating, by the vehicle monitor and based on theparameters of the multiple vehicle components and by the vehiclemonitor, whether a vehicle failure resulted or will result from anoperation of the vehicle that exceeds the warrantable operation of thetruck.

Step 4050 may include searching for a correlation between the vehiclefailure and a pattern of a usage of the vehicle.

Step 4060 of generating by the vehicle monitor a report about an outcomeof the determining and an outcome of the evaluating.

Step 4070 of transmitting, by a vehicle transmitter, the report. Thesize of the report is a fraction (for example less than 1/100 or 1/1000or 1/10000 of 1/1,000,000) of the aggregate size of the sensed vehicleparameters.

Step 4080 of receiving the report a remote computer.

Step 4090 of determining, by the remote computer a new warranty model.Step 4090 may be executed by the vehicle monitor.

Service Improvement by Better Incoming Diagnosis Data, Problem SpecificTraining and Technician Feedback

The trucking industry in the Americas is suffering an acute shortage ofqualified service technicians.

Most repair shops rely on training and certification of technicians toimprove performance.

Training and certification are a good starting point, but don't measurehow a technician actually uses the knowledge gained in training.

It has been found that by tracking and accurately rating eachtechnician's performance, the industry can help technicians do a betterjob and create a professional standing for technicians that in-turn willhelp recruit new technicians.

By improving the tools that technicians have to diagnose and repair atruck, technician effectiveness improves.

Original equipment manufacturers (OEMs) that use these improvements willincrease repair shop market-share and improve overall truck brand marketvalue.

There are provided systems, methods and computer readable medium thatstores instructions for accurately rating a technician's performance oneach job and providing accurate training advice. This is done bymeasuring the technician's use of resources on a job, normalized by thedifficulty of the job. This normalized measure helps illustrate thetechnician's performance correctly regardless of the difficulty of thejob. These normalized measures also, allow a technician to compare theirperformance to others doing similar work.

FIG. 20 illustrates method 5100 according to an embodiment of theinvention.

Step 5101 provides information used for both ranking the difficulty ofthe repair job and ranking the technician's performance.

Information on the incoming truck's state (problem indicated, etc.) fromsteps 5101 and 5104 is used with information from steps 5102 and 5103 torank the difficulty of the job (perhaps this should be shown as a stepof its own).

Step 5106 compares information from step 5101 related to the mechanic'sperformance (repair time, parts used, quality, comebacks, etc.) toinformation from the ranking of difficulty to rank the technician'sperformance.

Method 5100 may include the following steps:

Step 5101 of collecting information on each repair made by a giventechnician (problem indicated, repair time, parts used, quality,comebacks, etc.).

Step 5102 of using standard sources of information (e.g. flat ratemanual time) to rate repair difficulty.

Step 5103 of using empirical sources of information (e.g. peer mechanicperformance for the same job) to rate job difficulty.

Step 5104 of collecting data on incoming truck state to assist withproblem diagnosis.

Step 5105 of assisting diagnosis with tools such as fault trees based onactual field data, etc.

Step 5106 of comparing the technician's performance to standard andempirical measures of performance.

Step 5107 of creating a grading curve to show a given technician'soverall. performance among peers (e.g. histogram showing thetechnician's placement).

Step 5108 of reporting performance by other factors such as job type,truck make, age of truck, etc.

Step 5109 of use performance rankings to indicate strengths andweaknesses against different categories of repair jobs (search forcorrelations).

Step 5110 of suggesting training for improvement of weak areasidentified in step 5109.

Step 5111 of suggesting to serve as mentor or trainer in strong areas asidentified in step 5109.

Step 5112 of creating a “data-based resume” for each technician showingexperience, number of jobs completed, efficiency, quality, etc.

Step 5113 of collecting data from all technicians in a given shop tocreate measures of the shop's overall performance (e.g. flat-ratehours/week/technician, average quality, etc.).

Step 5114 of enabling a social network or chat-rook of technicians tocompare results.

Step 5115 of enabling a gamified repair environment for technicians tocompare, collaborate and compete with each other.

Step 5116 of creating a book of knowledge based on measured bestpractices from top technicians for each type of repair. Use technician'sinterviews, chat rooms, tweets, etc. to capture best practices.

Step 5117 of publishing technician ratings to draw customers (ref, Ebay,Uber, etc.).

Step 5101 may include collecting information from available sources tocharacterize the technician's performance on each repair. This includesinformation from a variety of sources such as, vehicle diagnostic codes,driver comments, repair orders, time and material usage trackers, OEMservice manuals, etc.

This step may include showing flow of data from driver point of view,truck point of view, technician point of view, show how flows intersectduring a repair event.]

Steps 5102 and 5103 (Use standard and empirical sources of informationto rate the job difficulty) may include estimating job difficulty bycombining one or more of the following parameters:

a. Flat-rate manual time (higher is generally more difficult)b. Difference between flat-rate time and repair time for the technicianin question and for the average of population of technicians (biggerdifferences are generally more difficult),c. Technician, service writer, shop manager opinions from surveys orsocial media,d. Variability of repair time for the technician in question and for thepopulation of technicians (higher variability is more difficult),e. Number of parts required (more parts means higher difficulty),f. Number system/sub-systems affected (more means higher difficulty)

The information mentioned above ((a)-(f)) may be combined in a number ofways:

a. Sum or weighted sum of the parameters,b. Dimensionless or dimensioned ratios of the parameters,c. Best fit using multiple regression of sub-tasks in each repair jobagainst overall difficulty or vice versa.

Step 5104 of collecting Data on incoming truck state may include:

1. Collect driver's comments,2. Collect truck base information (VIN, model year, model, driveline,other options, etc.)

3. Collect OBD DTCs,

4. Collect recent usage history (loads, routes, driver history of truckabuse, driver history of repairs needed on their truck, etc.), includedata from onboard telematics systems,

Collect repair history of truck and maintenance schedule.

Step 5105 of assist diagnostics may include:

1. Create a fault tree based on data in step 5104 and technician input,2. The fault tree can be guided by truck OEM diagnosis charts,3. The fault tree can be modified by actual repair histories (add ortrim branches as guided by field experience).4. The fault tree can show probabilities associated with each branch.

Step 5106 of Compare technician's performance to standard and empiricalmeasures of performance may include:

1. Compare technician's resource usage (time, parts, consumables) toflat rate manual, estimator's manual (less usage is better),2. Compare as in 1 with flat rate adjusted by field empirical fielddata,

Compare technician's performance on this job to similar jobs they havedone in the past.

Step 5107 of creating a grading curve to show a technician's performancevs peers may include:

1. Compare technicians individual repair or average repair performanceto peers using performance-to-grade function that yields high, mid, andlow grades in the desired proportions,2. Use a histogram (grade vs. number of samples) to show a technician'sperformance:a. vs. individual performances or average performances of peersb. vs. individual performances of themselves.

Step 5108 of Report technician performance against other factors mayinclude:

a. report the technician's performance against other factors such astruck model, driveline configuration, truck age, truck mileage, etc.,b. Search the space of other factors for correlations with performance.

FIGS. 21-22 illustrate method 5200 according to an embodiment of theinvention. Method 5200 includes steps 5202, 5206, 5210, 5214, 5218,5222, 5226, 5230, 5234, 5238, 5242, 5246, 5250, 5254, 5258, 5262, 5266,5270, 5274, 5278, 5280, 5281, 5282, 8283 and 5284:

Initiating a repair, when a truck is in need of repair or maintenance,we can initiate a repair or maintenance event from any of a number ofdifferent sources: high priority event detected by the Traffilogtelematics system (e.g. DTC), driver complaint, maintenance flag,inspection failure, etc. When a repair or maintenance event is initiatedproceed with the following steps. Each truck repair event is tracked asa transaction and all associated data described below is maintained aspart of the transaction record.

Using an in-vehicle telematics system, collect data on incoming truckstate to assist with problem diagnosis and for comparison to post-repairstate. Incoming data includes: recent truck background data (uptime,core truck efficiency, duty cycle/mission, etc.), DTCs, and driver abuse(the story that the driver never tells . . . ).

Collecting data on incoming truck long-term history (time in service,history of duty cycle/mission, repair history, environment/climate,etc.) to assist with problem diagnosis. From SAP, TMV, or similar,access data using commonly available web APIs. Collect related climateand geographic data that are commonly available from Web APIs. Receivedata from importer's/dealer network database, such as the Iveco databasein Europe.

Collecting information on capability and availability of all shops andtechnicians in the vicinity of the truck, which could plausibly make therepair or maintenance. Information on technician capability includes:each repair made by a given technician (problem indicated, repair time,parts used, quality, comebacks, etc.), overall technician ranking andtechnician ranking related to the current repair job. Information onshop capability includes history of dealing with similar repair ormaintenance events, availability of technician time and shop resourcesto make the repair or maintenance. Based on technician and shopcapability create a prioritized list of technician/shop opportunitiesfor making the repair or maintenance.

Allowing users to select the technician and shop to make the repair ormaintenance with advice from the prioritized list of technicians/shopsand in light of the user's logistics needs (time to repair, location,etc.).

Assisting the technician in diagnosis with tools such as fault treesbased on actual field data, etc., (e.g. CNH Assist, share fault treefrom current repair, each repair builds on previous fault tree)<Automatic improvement of fault tree based on transaction data andmachine learning techniques.>

Using, once the technician diagnoses the problem or makes themaintenance, standard sources of information (e.g. flat rate manualtime) to rate repair or maintenance difficulty.

Using empirical sources of information (e.g. peer mechanic performancefor the same job) to rate job difficulty.

Performing at least one after-completion tasks (After completion of thejob): collecting data on the truck's state (for comparison to pre-repairstate), compare the technician's performance to standard (flat-ratemanual) and empirical (other technician's performance) measures ofperformance, averages and standard deviation of similar repairs in termsof billable time of repair, parts used, time to get parts, time to startwork, time to get repair completed, shop safety, correctness of therepair (return rate), warranty related issues (time limit, OEM parts,etc.).

Based on this comparison, creating a grading curve to show a giventechnician's overall performance over time compared to himself andcompared to peers (e.g. grading curve showing the technician'splacement), by repair shop, by dealer network, by OEM, by region forthis type of repair. Share this comparison with the technician and otherauthorized, interested parties. For example, a particular technician maybe ranked at the top of all technicians with respect to brake repairs,but due to his lack of experience in engine repairs his overall ratingmay be very low. So it is important to show an overall rating thatcovers repairs made to any system on the truck in the correct contextfor technicians that only work on certain truck systems. It is alsoimportant to show each technician's performance over time to illustratehow the technician's work improves with experience.

Also, if sufficient data is available, reporting technician performanceby other factors such as job type, truck make, age of truck, etc.,

Using performance rankings to indicate strengths and weaknesses of eachtechnician against different categories of repair jobs (search forcorrelations). Categories of repair jobs include: inspections, brakes,tires, electrical lighting, electrical accessories, transmission,engine, chassis, trailer, suspension, axle, driveline, interior/seating,etc. Report strengths and weaknesses to the technician on a periodicbasis.

Given the truck's incoming state and history described above, suggestingways to understand root cause of the need for repair, this may involvefeedback to the driver, fleet manager, or OEM.

Suggesting training for technician improvement of weak areas identifiedin step 12, “top three” report card, and similar Traffilog report todrivers, based on performance. Handle this as private information forauthorized use only. The report would typically show improvements overtime, show problem areas and show ranking among other technicians.

Suggesting that a particular technician could serve as mentor or trainerto less-capable technicians, in strong areas

Creating a “data-based resume” for each technician showing experience,number of jobs completed, efficiency, quality, etc. for the technicianto use as a basis of job certifications or other purposes.

Collecting data from all technicians in a given shop to create measuresof the shop's overall performance (e.g. flat-rate hours/week/technician,average quality, etc.) workshop tools availability, efficiency gettingparts, parts availability, and safety.

Enabling a social network or chat-room of technicians to compareresults. Of particular interest in the social network, flag unusualfaults/repairs, flag errors in training, flag errors in fault-trees,etc. Enable technicians to compare performance results. Enable atechnician ranking system based on experience, quality, efficiency, etc.(rookie, some experience (newbie), much experience (qualifiedtechnician), top experience (master), most experience (trainer,foreman)).

Enabling a gamified repair environment for technicians to compare,collaborate and compete with each other. This could be enabled with areal-time status (“battlefield”) display of an entire shop indicatinglocation of each job, assigned technician, job difficulty, time used,parts used, technician comments.

Creating a book of knowledge (Wiki structure?) based on measured bestpractices from top technicians for each type of repair. Use technician'sinterviews, chat rooms, tweets, etc. to capture best practices. This isthe basis of a continuous the improvement process.

Publishing technician and shop ratings to draw customers (ref, Ebay,Uber, etc.)

FIG. 23 illustrates a system that may include a vehicle monitor 300,analysis computer 5410, first repair computer 5401, second repaircomputer 5403, vehicle information sources 5403, third repair computer5404 and training room computers 5405, that are coupled via network5400.

The number of computers, their types and/or their connectivity may varyfrom those illustrated in FIG. 23. The computers may be servers, desktopcomputers, laptop computers, dedicated computers, and the like.

Any of the repair computers are computers that are used during therepair of a vehicle failure. They may be computerized vehicle diagnosticsystems that are tailored to a specific brand or manufacturer ofvehicles, can be a multi-purpose diagnostic tool, can be a computer thatis used to report repairs and/or monitor the repair—such as monitoringtime of repair, parts that are used to repair the vehicle failure, andthe like.

The vehicle monitor may provide information about the truck failure,about the status of the truck before and/or after the truck failure,prove inputs about the impact of the repair—whether the repair succeededor failed, whether the repair partially succeeded, the amount ofimprovement in the vehicle status due to the repair, and the like.

The analysis computer 5410 may analyze information from the first tillthird repair computers, from the vehicle monitor 300, from the vehicleinformation sources 5403 and generate the technician grade or any othergrading or comparison information related to the repair, technician,shop, and the like.

The system of FIG. 23 is an example of a system that includes one ormore computers such as servers, laptops, desktop computers, and the likethat distributed cog information from available sources to characterizethe technician's performance on each repair.

This includes information from a variety of sources such as, vehiclediagnostic codes, driver comments, repair orders, time and materialusage trackers, OEM service manuals, etc.

The information sources may be, for example, sources related to thetruck diagnosis and repair, technician history database, analysisalgorithms, shop management database, repair order time, parts, suppliesdatabase and the like.

The one or more computer may host various types of software such asreport generating software, display software, apps on mobile platforms,etc.

The system may execute instructions linking data from a number ofsources related to the truck diagnosis and repair, technician historydatabase, analysis algorithms, report generating software, displaysoftware, apps on mobile platforms, etc.

The system may provide improved incoming diagnosis data and truckhistory. This helps technicians more quickly diagnose problems (fromshop management database, repair order time, parts, supplies)(normalizing factors include: flat rate manual time, historicalperformance by other technicians, service manager input, etc.)

The system may provide specialized training, using conventional trainingmaterials such as shop manuals, computer-based instruction, video clips,etc. (how) when a technician is faced with a repair they have never madebefore.

The system may provide an ECU dump analyzer responsive to short-termtrends, long-term trends, statistics, etc. (read ECU state, eventhistory and interpret what happened before and during failure).

The system includes an over the air diagnosis capability (pleaseelaborate) that allows a technician to observe truck operation remotelyover a wireless link. Allow the technician to observe in near-real timewhat the truck is doing while on the road. Allow a conversation betweenthe truck driver and the technician. Such a system may include a CAN busgateway that conducts CAN bus messages local to the truck across thewireless internet to a CAN bus analyzer or diagnostic tool, or to aserver-based CAN bus analyzer. The system may include an over-the-airpatch capability that allows a technician to push a diagnostic orlimp-home code to electronic controllers on the truck. For example, whena problem is detected in the urea doser in a selective catalyticreduction exhaust after treatment system, the system may automaticallypush limp-home software to the truck. In this case the doser is operatedto slightly over-dose the SCR catalyst with urea, which allows the truckto remain in compliance with emissions regulations at the cost higherurea fluid consumption. Once the problem with the doser is repaired,normal operating code is restored. In another example, the technicianmay need to collect more detailed information on the truck's operationto make a correct diagnosis. In this case, the system may automaticallypush diagnostic software to the truck, which transmits the neededinformation.

The system provides a measure of overall shop performance (describedabove) (please elaborate) by observing the performance of all thetechnicians in the shop. Similar to mechanic performance, measure keyperformance indicators of the overall shop (e.g. billable hours, flatrate time vs real time, overhead costs, etc.)

The system can provide measures of shop support activities such as partsprocurement, warranty claim clearance rates, etc.

The proposed system measures a technician's performance on each job,suggests training for improvement and allows a technician to comparetheir performance to others doing similar work.

The system also includes a number of tools to speed up problemdiagnosis, and reduce repair time. These include: incoming diagnosticsfrom truck to be repaired, ECU dump analyzer, and as needed training,delivered on the job.

The system improves the technician's capability and therefore theirprofessional standing. This also improves results for repair customersand shop owners.

The proposed system measures a technician's performance on each job,suggests training for improvement and allows a technician to comparetheir performance to others doing similar work.

The system also includes a number of tools to speed up problemdiagnosis, and reduce repair time. These include: incoming diagnosticsfrom truck to be repaired, ECU dump analyzer, and as needed training,delivered on the job.

FIG. 24 illustrates method 5500 according to an embodiment of theinvention.

Method 5500 may be used for failure analysis and technician assessment.

Method 5500 may include the steps of:

Step 5510 of sensing sensed vehicle parameters by multiple vehiclesensors that comprise multiple types of sensors.

Step 5520 of calculating, by a vehicle monitor, based on the sensedvehicle parameters, parameters of multiple vehicle components. Thevehicle monitor is mechanically coupled to the vehicle or installed inthe vehicle. The parameters of the multiple vehicle components providean indication about the status of the vehicle.

Step 5530 of searching, by the vehicle monitor and based on theparameters of the multiple vehicle components and by the vehiclemonitor, for a truck failure that is either a current truck failure oran impeding truck failure.

Step 5535 of selecting by the vehicle monitor a selected technician (orshop) to repair the truck failure or suggesting to a driver of thevehicle a suggested technician (or shop) to repair the truck failure.

Step 5540 of receiving, by the vehicle monitor, a notification that thetruck failure was repaired.

Step 5550 of estimating an impact of the repair by the vehicle monitorand based on at least parameters of multiple vehicle components that arecalculated from sensed vehicle parameters that are sensed after therepair.

Step 5560 of transmitting a report about the impact of the repair to acomputer located outside the vehicle.

Step 5550 may include comparing between a status of the vehicle beforethe repair and a status of the vehicle after the repair.

Step 5535 may include at least one of the following:

a. Receiving or generating, by the vehicle monitor, repair informationabout locations and technical skills of multiple technicians; andselecting by the vehicle monitor a selected technician based on thetechnician information and a location of the vehicle. The technicalskills of the multiple technicians may be related to a repair of thetruck failure.b. Receiving or generating, by the vehicle monitor, repair informationabout locations and technical skills of multiple technicians; andselecting by the vehicle monitor a selected technician based on at leastone truck failure attribute, the technician information, and a locationof the vehicle. The truck failure attribute may be a severity of thetruck failure. The truck failure is an impeding truck failure andwherein the selection may be based upon an estimated time till anoccurrence of the truck failure.c. Receiving or generating, by the vehicle monitor, repair informationabout locations and technical skills of multiple shops; and selecting bythe vehicle monitor a selected shop based on the shop information and alocation of the vehicle. The technical skills of the multiple shops arerelated to a repair of the truck failure.d. Receiving or generating, by the vehicle monitor, repair informationabout locations and technical skills of multiple shops; and selecting bythe vehicle monitor a selected shop based on at least one truck failureattribute, the shop information, and a location of the vehicle. Thetruck failure attribute may be a severity of the truck failure. Thetruck failure may be an impeding truck failure and wherein the selectionis based upon an estimated time till an occurrence of the truck failure.

FIG. 25 illustrates method 5600 according to an embodiment of theinvention.

Method 5600 may be used for grading a technician.

Method 5600 may include the following steps:

Step 5610 of sensing sensed vehicle parameters by multiple vehiclesensors that comprise multiple types of sensors.

Step 5620 of processing the sensed vehicle parameters by a vehiclemonitor that is coupled to the vehicle or included in the vehicle toprovide vehicle failure information.

Step 5630 of receiving repair information sensed by a vehicle repaircomputer, the repair information is about a repair of the truck failureby the technician.

Step 5640 of computing a repair grade that is related to the repair ofthe vehicle failure by the technician; wherein the computing is based onat least, the repair information and the vehicle failure information.

Step 5640 may include computing of the repair grade in response torepairs of the same vehicle failure by other technicians.

The invention may also be implemented in a computer program for runningon a computer system, at least including code portions for performingsteps of a method according to the invention when run on a programmableapparatus, such as a computer system or enabling a programmableapparatus to perform functions of a device or system according to theinvention.

The computer program may cause the storage system to allocate diskdrives to disk drive groups.

A computer program is a list of instructions such as a particularapplication program and/or an operating system. The computer program mayfor instance include one or more of: a subroutine, a function, aprocedure, an object method, an object implementation, an executableapplication, an applet, a servlet, a source code, an object code, ashared library/dynamic load library and/or other sequence ofinstructions designed for execution on a computer system.

The computer program may be stored internally on a non-transitorycomputer readable medium. All or some of the computer program may beprovided on computer readable media permanently, removably or remotelycoupled to an information processing system. The computer readable mediamay include, for example and without limitation, any number of thefollowing: magnetic storage media including disk and tape storage media;optical storage media such as compact disk media (e.g., CD-ROM, CD-R,etc.) and digital video disk storage media; nonvolatile memory storagemedia including semiconductor-based memory units such as FLASH memory,EEPROM, EPROM, ROM; ferromagnetic digital memories; MRAM; volatilestorage media including registers, buffers or caches, main memory, RAM,etc. A computer process typically includes an executing (running)program or portion of a program, current program values and stateinformation, and the resources used by the operating system to managethe execution of the process. An operating system (OS) is the softwarethat manages the sharing of the resources of a computer and providesprogrammers with an interface used to access those resources. Anoperating system processes system data and user input, and responds byallocating and managing tasks and internal system resources as a serviceto users and programs of the system. The computer system may forinstance include at least one processing unit, associated memory and anumber of input/output (I/O) devices. When executing the computerprogram, the computer system processes information according to thecomputer program and produces resultant output information via I/Odevices.

In the foregoing specification, the invention has been described withreference to specific examples of embodiments of the invention. It will,however, be evident that various modifications and changes may be madetherein without departing from the broader spirit and scope of theinvention as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under”and the like in the description and in the claims, if any, are used fordescriptive purposes and not necessarily for describing permanentrelative positions. It is understood that the terms so used areinterchangeable under appropriate circumstances such that theembodiments of the invention described herein are, for example, capableof operation in other orientations than those illustrated or otherwisedescribed herein.

Those skilled in the art will recognize that the boundaries betweenlogic blocks are merely illustrative and that alternative embodimentsmay merge logic blocks or circuit elements or impose an alternatedecomposition of functionality upon various logic blocks or circuitelements. Thus, it is to be understood that the architectures depictedherein are merely exemplary, and that in fact many other architecturesmay be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality may be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundariesbetween the above described operations merely illustrative. The multipleoperations may be combined into a single operation, a single operationmay be distributed in additional operations and operations may beexecuted at least partially overlapping in time. Moreover, alternativeembodiments may include multiple instances of a particular operation,and the order of operations may be altered in various other embodiments.

Also for example, in one embodiment, the illustrated examples may beimplemented as circuitry located on a single integrated circuit orwithin a same device. Alternatively, the examples may be implemented asany number of separate integrated circuits or separate devicesinterconnected with each other in a suitable manner.

Also for example, the examples, or portions thereof, may implemented assoft or code representations of physical circuitry or of logicalrepresentations convertible into physical circuitry, such as in ahardware description language of any appropriate type.

Also, the invention is not limited to physical devices or unitsimplemented in non-programmable hardware but can also be applied inprogrammable devices or units able to perform the desired devicefunctions by operating in accordance with suitable program code, such asmainframes, minicomputers, servers, workstations, personal computers,notepads, personal digital assistants, electronic games, and otherembedded systems, cell phones and various other wireless devices,commonly denoted in this application as ‘computer systems’.

However, other modifications, variations and alternatives are alsopossible. The specifications and drawings are, accordingly, to beregarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘comprising’ does notexclude the presence of other elements or steps then those listed in aclaim. Furthermore, the terms “a” or “an,” as used herein, are definedas one or more than one. Also, the use of introductory phrases such as“at least one” and “one or more” in the claims should not be construedto imply that the introduction of another claim element by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim element to inventions containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an.”The same holds true for the use of definite articles. Unless statedotherwise, terms such as “first” and “second” are used to arbitrarilydistinguish between the elements such terms describe. Thus, these termsare not necessarily intended to indicate temporal or otherprioritization of such elements. The mere fact that certain measures arerecited in mutually different claims does not indicate that acombination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

We claim:
 1. A method for failure analysis and warranty claimassessment, the method comprises: sensing sensed vehicle parameters bymultiple vehicle sensors that comprise multiple types of sensors;calculating, by a vehicle monitor, based on the sensed vehicleparameters, parameters of multiple vehicle components; wherein thevehicle monitor is mechanically coupled to a vehicle or installed in thevehicle; determining, by the vehicle monitor and based on the parametersof the multiple vehicle components, whether the operation of the vehicleexceeds a warrantable operation of the vehicle; and evaluating, by thevehicle monitor and based on the parameters of the multiple vehiclecomponents and by the vehicle monitor, whether a vehicle failureresulted from an operation of the vehicle that exceeds the warrantableoperation of the vehicle or will result from the operation of thevehicle that exceeds the warrantable operation of the vehicle.
 2. Themethod according to claim 1 wherein the sensed vehicle parameters areduty related parameters and wherein the method comprises determining anactual wear of the vehicle, by the vehicle monitor and based on theparameters of multiple vehicle components.
 3. The method according toclaim 1 comprising generating, by the vehicle monitor and in real time,an alert about the vehicle failure.
 4. The method according to claim 1comprising generating by the vehicle monitor and in real time, an alertabout an expected vehicle failure before the vehicle failure occurs. 5.The method according to claim 1 comprising instructing the vehicle, bythe vehicle monitor, to change a vehicle parameter if the vehiclemonitor determines that a vehicle failure occurs or if a vehicle failureis impeding.
 6. The method according to claim 1 wherein the vehiclefailure is a vehicle system failure and wherein the method comprisesinforming, by the vehicle monitor, a vehicle controller that isresponsible to the vehicle system about the vehicle system failure. 7.The method according to claim 1 wherein the evaluating comprisessearching for a correlation between the vehicle failure and a pattern ofa usage of the vehicle.
 8. The method according to claim 1 comprisinggenerating by the vehicle monitor a report about an outcome of thedetermining and an outcome of the evaluating.
 9. The method according toclaim 8 comprising: transmitting, by a vehicle transmitter, the report;and receiving the report a remote computer; and determining, by theremote computer a new warranty model.
 10. The method according to claim8 wherein a size of the report is less than one thousandth of anaggregate size of the sensed vehicle parameters.
 11. The methodaccording to claim 8 wherein a size of the report is less than onemillionth of an aggregate size of the sensed vehicle parameters.
 12. Acomputer program product that is non-transitory and stores instructionsthat once executed by a vehicle monitor causes the vehicle monitor toperform the steps of: receiving sensed vehicle parameters that aresensed by multiple vehicle sensors that comprise multiple types ofsensors; calculating, based on the sensed vehicle parameters, parametersof multiple vehicle components; wherein the vehicle monitor ismechanically coupled to a vehicle or installed in the vehicle;determining, based on the parameters of the multiple vehicle components,whether the operation of the vehicle exceeds a warrantable operation ofthe vehicle; and evaluating, based on the parameters of the multiplevehicle components and by the vehicle monitor, whether a vehicle failureresulted from an operation of the vehicle that exceeds the warrantableoperation of the vehicle or will result from the operation of thevehicle that exceeds the warrantable operation of the vehicle.
 13. Thecomputer program product according to claim 12 wherein the sensedvehicle parameters are duty related parameters and wherein the computerprogram product stores instructions for determining an actual wear ofthe vehicle, by the vehicle monitor and based on the parameters ofmultiple vehicle components.
 14. The computer program product accordingto claim 12 that stores instructions for generating, by the vehiclemonitor and in real time, an alert about the vehicle failure.
 15. Thecomputer program product according to claim 12 that stores instructionsfor generating by the vehicle monitor and in real time, an alert aboutan expected vehicle failure before the vehicle failure occurs.
 16. Thecomputer program product according to claim 12 that stores instructionsfor instructing the vehicle, by the vehicle monitor, to change a vehicleparameter if the vehicle monitor determines that a vehicle failureoccurs or if a vehicle failure is impeding.
 17. The computer programproduct according to claim 12 wherein the vehicle failure is a vehiclesystem failure and wherein the computer program product storesinstructions for informing a vehicle controller that is responsible tothe vehicle system about the vehicle system failure.
 18. The computerprogram product according to claim 12 wherein the computer programproduct stores instructions for searching for a correlation between thevehicle failure and a pattern of a usage of the vehicle.
 19. Thecomputer program product according to claim 12 that stores instructionsfor generating a report about an outcome of the determining and anoutcome of the evaluating.
 20. The computer program product according toclaim 19 stores instructions for determining a new warranty model. 21.(canceled)
 22. (canceled)